CN115860869A - Shop information recommendation method, equipment and storage medium - Google Patents

Abstract

The embodiment of the application provides a shop information recommendation method, equipment and a storage medium. In the embodiment of the application, the media event is fused into the store recommendation process, so that more stores related to the media event can be recommended to users, and the enrichment of store recommendation is realized; in addition, when the stores are recommended to the users, the media store information is displayed to the users, the attraction of the stores to the users is increased through the media store information, the probability of the users accessing the stores is improved, the efficiency of the users obtaining the needed commodities from the stores is improved, and the use experience of the users on the E-commerce applications is improved. Furthermore, the exposure rate and the user flow of the shop can be increased, and the sales data of the shop can be improved.

Description

Shop information recommendation method, equipment and storage medium

Technical Field

The application relates to the technical field of internet, in particular to a shop information recommendation method, equipment and a storage medium.

Background

In e-commerce applications, the number of stores is large, and a plurality of stores sell the same or similar goods, so that for consumers, it is time-consuming and laborious to select a desired store from a plurality of stores, and the physical examination of the shopping is poor.

In order to improve shopping experience of a consumer, the existing e-commerce application starts a store recommendation function, and performs personalized store recommendation for the consumer according to the preference of the consumer for commodities, so that the time of the consumer for shopping is shortened to a certain extent.

However, the store recommendation based on the preference of the consumer for the product results in a somewhat blocked store discovery path, and the consumer is difficult to discover a richer store, thereby reducing the use experience of the e-commerce application.

Disclosure of Invention

Aspects of the application provide a store information recommendation method, equipment and a storage medium, which are used for improving the richness of store recommendation, improving the efficiency of obtaining required commodities from stores by users and further improving the use experience of the users on e-commerce applications.

The embodiment of the application provides a shop information recommendation method, which comprises the following steps: responding to trigger operation recommended by a shop, and acquiring user behavior data and/or shop sales data of at least one shop in a specified time period; acquiring a target store of the associated media event from the at least one store according to the user behavior data and/or the store sales data of the at least one store in a specified period; generating medialized shop information of the target shop according to the media event related to the target shop, wherein the medialized shop information comprises information related to the media event; displaying the medialized shop information of the target shop on a first page to recommend the target shop to a user.

The embodiment of the application further provides a shop information recommendation method, which comprises the following steps: acquiring a target media event occurring in a specified time period; determining a targeted store associated with the targeted media event from at least one store; generating medialized shop information of the target shop according to the target media event, wherein the medialized shop information comprises information related to the target media event; displaying the medialized shop information of the target shop on a first page to recommend the target shop to a user.

The embodiment of the application further provides a shop information recommendation method, which comprises the following steps: responding to trigger operation recommended by a shop, and sending a shop recommendation request to the server equipment; receiving medialization information of a target shop returned by the server-side equipment, wherein the target shop is a shop which is determined by the server-side equipment according to user behavior data and/or shop sales data of at least one shop in a specified period and is related to a media event, and the medialization shop information is generated according to the media event related to the target shop; displaying the medialized shop information of the target shop on a first page to recommend the target shop to a user.

The embodiment of the application further provides a shop information recommendation method, which comprises the following steps: responding to a shop recommendation request sent by terminal equipment, and acquiring user behavior data and/or shop sales data of at least one shop in a specified period; acquiring a target shop of the associated media event from the at least one shop according to the user behavior data and/or the shop sales data of the at least one shop in a specified period of time; generating medialized shop information of the target shop according to the media event related to the target shop, wherein the medialized shop information comprises information related to the media event; and sending the medialized shop information of the target shop to the terminal equipment so that the terminal equipment can display the medialized shop information of the target shop.

An embodiment of the present application further provides an electronic device, including: a memory and a processor; the memory for storing a computer program; the processor is coupled with the memory and is used for executing the computer program to realize the steps in any shop information recommendation method provided by the embodiment of the application.

Embodiments of the present application further provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to implement any of the steps in the store information recommendation method provided in the embodiments of the present application.

In the embodiment of the application, a target store related to a media event is determined according to user behavior data and/or store sales data of the store in a designated period, or a target media event is selected from the media events and determined, the medialized store information of the target store is generated according to the media event related to the target store, the target store is recommended to a user based on the medialized store information, and more stores related to the media event can be recommended to the user by integrating the media event into a store recommendation process, so that the enrichment of store recommendation is realized; in addition, when the stores are recommended to the users, the media store information is displayed to the users, the attraction of the stores to the users is increased through the media store information, the probability of the users accessing the stores is improved, the efficiency of the users obtaining the needed commodities from the stores is improved, and the use experience of the users on the E-commerce applications is improved. Furthermore, the exposure rate and the user flow of the shop can be increased, and the sales data of the shop can be improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a schematic diagram of a shop information recommendation scene according to an embodiment of the present application;

fig. 2a is a schematic flowchart of a store information recommendation method according to an embodiment of the present application;

FIG. 2b is a schematic diagram of a store recommendation result page according to an embodiment of the present application;

FIG. 2c is a schematic diagram of another shop recommendation result page provided in the embodiments of the present application;

FIG. 2d is a schematic diagram of another shop recommendation result page provided in the embodiment of the present application;

FIG. 2e is a schematic flow chart of another store information recommendation method according to an embodiment of the present application;

FIG. 2f is a schematic diagram of a store syndication page provided by an embodiment of the application;

FIG. 3 is a schematic flow chart of another shop information recommendation method according to an embodiment of the present application;

FIG. 4 is a schematic flow chart illustrating a further shop information recommendation method according to an embodiment of the present application;

fig. 5a is a schematic structural diagram of a store information recommendation device according to an embodiment of the present application;

FIG. 5b is a schematic structural diagram of another store information recommendation device according to an embodiment of the present application;

fig. 5c is a schematic structural diagram of another shop information recommendation device according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

In the traditional store recommendation process, a store finding path is blocked, so that a consumer user can hardly find a richer store, and the use experience of e-commerce application is reduced. Aiming at the technical problem, the embodiment of the application provides a shop information recommendation method, equipment and a storage medium, wherein a media event is fused into a shop recommendation process, so that more shops related to the media event can be recommended to a user, and the enrichment of shop recommendation is realized; in addition, when the store is recommended to the user, the media-implemented store information is displayed to the user, the attraction of the store to the user is increased through the media-implemented store information, the probability of store access of the user is improved, the efficiency of obtaining the needed goods from the store by the user is improved, and the use experience of the user on the E-commerce application is further improved. Furthermore, the exposure rate and the user flow of the shop can be increased, and the sales data of the shop can be improved.

The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

Fig. 1 is a schematic view of a shop information recommendation scene provided in an embodiment of the present application. As shown in fig. 1, in this scenario, a terminal device 11a and a server device 11b are included. The terminal device 11a may interact with the server device 11b through a wired network or a wireless network. For example, the wired network may include coaxial cables, twisted pair wires, optical fibers, and the like, and the Wireless network may be a 2G network, a 3G network, a 4G network, or a 5G network, a Wireless Fidelity (WIFI) network, and the like.

Alternatively, the terminal device 11a may be, but is not limited to: the mobile phone, the tablet computer, the notebook computer, the wearable device and the vehicle-mounted device. The server device 11b may be, but is not limited to: a traditional server, a cloud server, an array or cluster of servers, etc. It should be understood that the number of the terminal devices 11a and the server devices 11b and the product form in fig. 1 are only illustrative and not limited thereto.

The user can browse the commodity information and the store information through an electronic commerce Application (APP) installed in the terminal device 11a, select the commodity information on line, and perform operations such as ordering and paying for the selected commodity. The e-commerce APP can provide a plurality of pages to the user, such as a home page, a message page, a commodity detail page, a merchant shop page, a shopping cart page, various recommendation pages and the like. In the embodiment of the application, the e-commerce APP can also provide a store recommendation function for the user so as to recommend the store to the user. In the embodiment of the application, the shop refers to an online shop opened by a merchant on an e-commerce APP, and has functions of managing commodity information and shop information, displaying the commodity information, shopping cart function, ordering payment and the like. From the page dimension, a store is a collection of pages that manage various pages, such as a shop front page, a product detail page, a product category page, and the like.

Referring to fig. 1, a user may initiate a trigger operation of a store recommendation on a specific page of an e-commerce APP, where the specific page includes, but is not limited to, a home page, a product detail page, a search page, and the like, and a store recommendation function portal is included on the specific page, through which the user may initiate the trigger operation of the store recommendation. Illustratively, as shown in fig. 1, a word "good daily store" is included in the function navigation area of the top page, the "good daily store" is an example of a store recommendation function entrance, and a user can click on the "good daily store" to initiate a trigger operation of store recommendation; alternatively, the user may also enter "good stores a day" in the search bar on the home page, and then click on the "search" control to initiate a trigger action for store recommendations.

When the user initiates a trigger operation for store recommendation, referring to (1) in fig. 1, the terminal apparatus 11a transmits a store recommendation request to the server apparatus 11b in response to the trigger operation for store recommendation. Referring to (2) in fig. 1, the server apparatus 11b acquires user behavior data and/or store sales data of at least one store in a specified period of time in response to a store recommendation request. Next, referring to (3) in fig. 1, the server apparatus 11b obtains a target store of the associated media event from at least one store according to the user behavior data and/or the store sales data of the at least one store in a specified period. Next, referring to (4) in fig. 1, the server apparatus 11b generates, from the media event associated with the target store, medialized store information of the target store, the medialized store information including information associated with the media event. Next, referring to (5) in fig. 1, the server apparatus 11b transmits the medialized store information of the target store to the terminal apparatus 11a. Continuing to refer to (6) in fig. 1, the terminal device 11a, upon receiving the medialized store information of the targeted store, presents the medialized store information of the targeted store on a first page to recommend the targeted store to the user. The first page is a shop recommendation result page and comprises the medialized shop information of at least one target shop.

Here, in the application scenario shown in fig. 1, the shop information recommendation process is completed by the terminal device 11a and the server device 11b in cooperation, but the invention is not limited thereto. The shop information recommendation process may be performed by the terminal device 11a independently, and in the case where the terminal device 11a is performed independently, the operation originally performed by the server device 11b is performed by the terminal device 11a. For a detailed process of store information recommendation, reference is made to the description of the method embodiment below.

Fig. 2a is a schematic flow chart of a store information recommendation method according to an embodiment of the present application. The shop information recommendation method provided by the embodiment can be implemented by the terminal device alone, or can be completed by the terminal device and the server device in cooperation. As shown in fig. 2a, the method comprises:

201. responding to the trigger operation recommended by the shop, and acquiring user behavior data and/or shop sales data of at least one shop in a specified period;

202. acquiring a target shop of the associated media event from at least one shop according to the user behavior data and/or the shop sales data of the at least one shop in a specified period of time;

203. generating medialized shop information of a target shop according to a media event related to the target shop, wherein the medialized shop information comprises information related to the media event;

204. the media shop information of the target shop is shown on the first page to recommend the target shop to the user.

In this embodiment, in the process that a user uses an e-commerce APP installed on a terminal device, if the e-commerce APP is required to recommend a store for the user, a trigger operation recommended by the store can be initiated to activate a store recommendation function of the e-commerce APP. The manner of the trigger operation of the store recommendation initiated by the user is not limited, for example, a store recommendation function entrance is provided on a specific page, for example, but not limited to, a "daily good store" function entrance is provided on a top page, and the user can initiate the trigger operation of the store recommendation through the store recommendation function entrance on the specific page; for another example, when the e-commerce APP supports the voice recognition function, the user may initiate the trigger operation of the store recommendation by a voice command, for example, but not limited to, the trigger operation of the store recommendation may be initiated by voice information of "store recommendation".

In the embodiment, when a user initiates a trigger operation of store recommendation, the terminal device can execute a store recommendation function of the e-commerce APP to perform store recommendation for the user. Specifically, the terminal device can respond to the trigger operation recommended by the shop and acquire the heat representation data of at least one shop in a specified period. At least one store forms an initial store set for performing store recommendation in the embodiment of the present application, and the initial store set may be all stores provided by the e-commerce APP or part of stores, which is not limited herein. When the initial shop set is a part of shops provided by the e-commerce APP, the initial shop set required by the shop recommendation can be formed by flexibly selecting a part of shops according to application requirements. The manner in which the selection of a portion of the stores to form the initial set of stores is illustrated, including but not limited to any one of the following or a combination of at least two of the following:

mode a1: the shop in the normal business state when the user initiates the shop recommending operation can be selected, and the shop which is out of business or closed does not pay attention.

Mode a2: according to the current position of the user, the shop with the service range capable of covering the current position of the user can be selected, and the shop with the service range incapable of covering the current position of the user is not concerned.

Mode a3: according to the commodity demand information of the user, the shop capable of providing the required commodity information for the user can be selected, and the shop incapable of providing the required commodity information for the user is not concerned. The commodity requirement information of the user can be determined according to portrait data of the user and/or network behavior data in a certain period of time.

Mode a4: according to the current position of the user, the shop with the service range capable of covering the current position of the user is selected from the shops in the normal business state when the user initiates the shop recommendation operation, and the shop with the business suspension or closing and the shop with the service range incapable of covering the current position of the user are not concerned.

Mode a5: according to the current position of the user and the commodity requirement information, a shop with a service range capable of covering the current position of the user and providing the needed commodity information for the user is selected from shops in a normal business state when the user initiates shop recommendation operation, and the shop with a service range incapable of covering the current position of the user and the shops incapable of providing the needed commodity information for the user does not pay attention to shops which are out of business or closed, shops with a service range incapable of covering the current position of the user and the shops incapable of providing the needed commodity information for the user.

Mode a6: the stores not having the first specific attribute may be selected based on the attribute information of each store, and the stores having the first specific attribute may not be paid attention to. For example, the first specific attribute may be a low-ranking store attribute, a sports brand, or a store attribute that has no new merchandise information in the near future.

Mode a7: the stores having the second specific attribute may be selected based on the attribute information of each store, and the stores not having the second specific attribute may not be paid attention to. For example, the second specific attribute may be a specific business category, such as women's clothing or children's garments, and may be a store attribute where the business hours are greater than 10 years, or where the return customers exceed 2 ten thousand.

In this embodiment, the heat characterization data for each store may include, but is not limited to: user behavior data and/or store sales data for each store over a specified period of time. The designated time period can be flexibly set according to application requirements, and preferably, can be the latest period of time, such as the latest week, the latest ten days, the latest month or the latest three months. For any store, the user behavior data includes various behavior data generated by various users visiting the store within a specified time period, including but not limited to: store visits, commodity browsing, shopping cart adding, ordering, payment, comment, consultation and other various behavior data, wherein the behavior data comprises user information initiating the behavior data, the type of the behavior data, the time of occurrence of the behavior data, objects (such as stores, commodity information or payment amount) related to the behavior data and the like. The method comprises the steps of calculating user behavior data, such as calculating how many users have initiated visiting operations to shops in a specified time period, calculating how many users have performed browsing operations, shopping cart adding operations, ordering operations or payment operations in the shops in the specified time period, and obtaining visiting heat information of the shops according to the statistical information; furthermore, it is also possible to count which commodity information the user browses and the number of times the commodity information is browsed in the store in a specified time period, which commodity information the user adds to a shopping cart and the number of times the commodity information is added to the shopping cart, which commodity information the user places an order and the number of times the commodity information is placed, which commodity information the user performs a payment operation on and the number of times the commodity information is paid, and the like, and it is possible to know the access heat information of each commodity information in the store from these pieces of statistical information. For any shop, the shop sales data includes sales, sales amount, order amount, and the like of each statistical dimension generated by the shop in a specified period of time. The shop sales data may reflect the visit rate information of the shop in a predetermined period, and the larger the sales volume, sales amount, or order volume, the larger the number of users visiting the shop and the number of users placing orders, paying, or the like for the product information in the shop, that is, the higher the visit rate of the shop.

In the embodiment of the application, when the shop recommendation is carried out, the user behavior data of the shop in the appointed time period can be independently used, and the user behavior data can reflect the visit heat information of the shop in the appointed time period; or, the shop sales data of the shop in the appointed time period can be used independently, and the shop sales data can reflect the visit heat information of the shop in the appointed time period; alternatively, both the user behavior data and the store sales data of the store during a specified period of time are used.

After obtaining the user behavior data and/or the store sales data of the at least one store in the designated period of time, the store associated with the media event may be obtained from the at least one store according to the user behavior data and/or the store sales data of the at least one store in the designated period of time, and for convenience of description and distinction, the store associated with the media event is referred to as a target store. The known media event includes at least one acquired media event, and the media event refers to an event with a certain popularity, which is propagated to the public by taking mass media or a network as a medium and a channel, and may be, for example, an entertainment event, a news event, a social event, and the like. Each media event has respective description information for describing the content and attributes of the media event. Optionally, various media events and their description information may be crawled from the network by a web crawler, or may cooperate with a social platform and a media platform that push various media events, and register with the social platform and the media platform in advance, and push various media events and their description information by the social platform and the media platform. The description information of the media event includes attribute information of the media event, a title of the media event, a teletext content, audio information and/or video information.

In the embodiment of the application, when the target shop is obtained, at least one candidate shop can be selected from at least one shop according to the user behavior data and/or the shop sales data of the at least one shop in a specified period; at least one candidate store is then associated with the known media event, and a target store associated with the media event is then obtained therefrom. Further, the visit rate information of the at least one shop can be generated according to the user behavior data and/or the shop sales data of the at least one shop in the appointed time period; and selecting at least one candidate shop meeting the popularity condition from the at least one shop according to the visit popularity information of the at least one shop.

For example, the user behavior data and/or the store sales data of the store in a specified period may be counted to obtain numerical data such as total number of visits, total number of orders placed, total number of browses, total sales amount, and the like of the store, and then the numerical data may be normalized to obtain normalized data as the visit heat information of the store; alternatively, these numerical data may be directly used as the visit-heat information of the shop; alternatively, the numerical data may be subjected to weighted summation, and the result of the weighted summation may be used as the visit-heat information of the store. Then, according to the visit heat degree information of at least one store and the number of the set candidate stores, selecting at least one store with the largest visit heat degree as a candidate store; alternatively, a store having visit-heat-degree information greater than a set visit-heat-degree threshold may be selected as a candidate store from among the visit-heat-degree information of at least one store.

Further alternatively, when at least one candidate store meeting the heat condition is selected from the at least one store according to the visit heat information of the at least one store, in addition to the visit heat information of the store, multidimensional store attributes including the visit heat information may be comprehensively considered, for example, the multidimensional store attributes are weighted and summed to obtain a comprehensive quality score of the store, and at least one store with the highest comprehensive quality score is selected as a candidate, in combination with other attribute information of the store, such as the type, the main category, the operation age, the goodness-of-appreciation and the like of the store.

After the candidate stores are selected, at least one of the candidate stores may be associated with a known media event to obtain a targeted store associated with the media event. In particular, when associating at least one candidate store with a known media event, the at least one candidate store may be associated with the known media event from at least one information dimension. Wherein the at least one information dimension is associated with the known media event based on information of the at least one dimension of the candidate store, primarily from the perspective of the candidate store. For any of the candidate stores, there may or may not be a successful association to a media event. Alternatively, a candidate store associated with the media event may be targeted as the targeted store, or a candidate store associated with the media event while satisfying other filtering conditions may be targeted as the targeted store. Among other screening conditions, but not limited to: the type of store, the size of the store, etc. For the same target store, the target store can be associated with one media event or a plurality of media events at the same time; it is possible for different targeted stores to be associated with the same media event or to be associated with different media events.

Further optionally, associating the at least one candidate store with known media events from at least one information dimension, including but not limited to the following embodiments:

mode b1: and for any candidate store, performing information matching on the information of at least one dimension of the candidate store and the description information of each known media event, and if the description information of the known media event in the matching is obtained, taking the candidate store as a target store associated with the known media event in the matching.

Mode b2: extracting target attribute words from the description information of the known media events, wherein the target attribute words comprise shop attribute words and/or commodity attribute words; aiming at any candidate shop, matching information of at least one dimension of the candidate shop with a target attribute word; and if the target attribute words are matched, taking the candidate shop as a target shop related to the known media events corresponding to the target attribute words.

In modes b1 and b2, the information of at least one dimension of the candidate stores includes, but is not limited to: attribute information of the candidate stores and/or commodity information in the candidate stores. The attribute information of the candidate shop belongs to information of one dimension, and the commodity information in the candidate shop belongs to information of another dimension. In addition, the attribute information of the candidate shop comprises a plurality of attribute information such as type, main category, operation year, heat degree and the like; the product information in the candidate store includes information such as a picture of each product, product description information (for example, detailed information such as a product name, a price, and specifications), and a comment.

In the method b1, information matching is directly performed with the description information of the known media event according to the attribute information of the candidate store and/or the commodity information in the candidate store. Optionally, a specific matching manner includes: extracting keywords from the attribute information of the candidate stores and/or the commodity information in the candidate stores to obtain a keyword set; segmenting words of description information of known media events to obtain a segmented word set; matching every two keywords in the keyword set with the participles in the participle set, then counting the probability in the matching, and determining whether the candidate shop is associated with the known media event according to the probability in the matching. For example, if the probability in the match is greater than a set probability threshold, determining that the candidate store is associated with the known media event; conversely, if the probability in the match is less than or equal to the set probability threshold, it is determined that the candidate store cannot be associated with the known media event.

In the mode b2, target attribute words, namely store attribute words and/or commodity attribute words, are extracted from the description information of the known media events; and then, matching with the target attribute word according to the attribute information of the candidate shop and/or the commodity information in the candidate shop. The commodity attribute word refers to a word which appears in the description information of the known media event and can embody specific commodity information to some extent, and may be, for example and without limitation: terms such as the name of the good, the model, the category, the manufacturer, the hyphen, etc., appear in the description information of the media event. The store attribute word refers to a word appearing in the description information of the known media event and capable of embodying specific store information to some extent, and may be, for example and without limitation: the words such as store name, major category, nickname, publicity words, etc. appear in the description information of the media event.

Optionally, in the mode b2, an embodiment of extracting the target attribute word from the description information of the known media event includes: and identifying the attribute information and the content information of the known media event from the description information of the known media event, and extracting target attribute words from the attribute information and the content information respectively. Further, under the condition that the attribute information or the content information contains picture, audio and/or video information, the picture, the audio and/or the video information in the attribute information or the content information are respectively converted into text information, the text information obtained through conversion and the text information contained in the original text in the attribute information and the content information are segmented, the segmentation is carried out on the segmentation to obtain a noun or a noun phrase, and the noun or the noun phrase is subjected to word meaning recognition, so that shop attribute words and/or commodity attribute words are obtained.

Optionally, in the mode b2, a mode of matching the target attribute word according to the attribute information of the candidate store and/or the commodity information in the candidate store includes:

a method for matching attribute information of candidate stores with target attribute words specifically comprises the following steps: under the condition that the attribute information of the candidate shop comprises pictures, audio and/or video information, converting the pictures, the audio and/or the video information, segmenting the text information obtained by conversion and the text information contained in the original text in the attribute information of the candidate shop, selecting nouns or noun phrases from the segmented words, respectively matching the selected nouns or noun phrases with the target attribute words, and determining the matching degree of the attribute information of the candidate shop and the target attribute words according to the number in matching; if the matching degree is greater than a set first matching degree threshold value, determining that the candidate shop is associated with the media event corresponding to the target attribute word; otherwise, if the matching degree is smaller than or equal to the set first matching degree threshold value, the candidate shop is determined to be irrelevant to the media event corresponding to the target attribute word.

Another way of matching the commodity information in the candidate store with the target attribute words is specifically: for each commodity information in the candidate shop, under the condition that the commodity information contains picture, audio and/or video information, converting the picture, audio and/or video information, segmenting the text information obtained by conversion and the text information originally contained in the commodity information, selecting nouns or noun phrases from the segmentation, respectively matching the selected nouns or noun phrases with the target attribute words, and determining the matching degree of the commodity information and the target attribute words according to the number in matching; if the matching degree is greater than a set second matching degree threshold value, determining that the commodity information is associated with the media event corresponding to the target attribute word; otherwise, if the matching degree is smaller than or equal to the set second matching degree threshold value, the commodity information is determined to be irrelevant to the media event corresponding to the target attribute word. Determining that the candidate shop is associated with the media event corresponding to the target attribute word under the condition that commodity information associated with the media event corresponding to the target attribute word exists in the candidate shop; otherwise, the candidate shop is determined not to be related to the media event corresponding to the target attribute word. Or when commodity information related to the media event corresponding to the target attribute word exists in the candidate shop and the number of the commodity information related to the media event is larger than a set number threshold value, determining that the candidate shop is related to the media event; otherwise, the candidate shop is determined not to be related to the media event corresponding to the target attribute word.

In another embodiment, the method for matching the target attribute word with the attribute information of the candidate store and the product information in the candidate store specifically includes: respectively matching the attribute information of the candidate shop and the commodity information in the candidate shop with the target attribute words; the process of matching the attribute information of the candidate store with the target attribute word and the process of matching the commodity information of the candidate store with the target attribute word refer to the above description, and are not repeated herein. Determining that the candidate shop is associated with the media event corresponding to the target attribute word when commodity information associated with the media event corresponding to the target attribute word exists in the candidate shop or when the matching degree of the attribute information of the candidate shop and the target attribute word is larger than a first matching degree threshold value; otherwise, the candidate shop is determined not to be related to the media event corresponding to the target attribute word. Or, when commodity information associated with the media event corresponding to the target attribute word exists in the candidate store and the number of the commodity information associated with the media event is greater than a set number threshold, determining that the candidate store is associated with the media event corresponding to the target attribute word; otherwise, the candidate shop is determined not to be related to the media event corresponding to the target attribute word.

After the targeted store associated with the media event is obtained, the medialized store information of the targeted store can be generated according to the media event associated with the targeted store. The medialized store information is a new form of store information generated by combining a media event with conventional store information. Wherein the traditional store information includes only information related to the store itself, including, for example and without limitation: store names, store icons or head portraits, the goodwill of the stores, the business years of the stores, and the like. Compared with the traditional shop information, the medialized shop information not only contains the traditional shop information, but also comprises information related to the media event, and the attention of the user to the shop is attracted through the media event. Then, the medialized shop information of the target shop is displayed on the first page so as to achieve the aim of recommending the target shop to the user.

In the present embodiment, the method of generating the medialized store information of the targeted store based on the media event associated with the targeted store is not limited, and the type and number of information related to the media event included in the medialized store information are not limited. The following exemplifies a method of generating the media store information:

mode c1: and according to the association degree of each commodity information and the media event in the target store, selecting at least one commodity information with the association degree meeting the requirement, and according to the description information of the media event, generating commodity file information of at least one commodity information as media store information.

Mode c2: store file information associated with the media event is generated as media store information according to the attribute information of the target store and the description information of the media event.

Mode c3: and selecting at least one commodity information with the heat information meeting the requirement from the heat information of each commodity information in the target store, and generating the commodity file information of the at least one commodity information as the media store information according to the description information of the media event.

In the aspect c1, the dimension of the commodity information is fused with the media event to form the medialized shop information. Optionally, whether each commodity information is associated with the media event or not and the association degree of the commodity information and the media event if the commodity information is associated with the media event can be analyzed according to the description information of each commodity information and the media event in the target store; then, selecting at least one commodity information with the association degree meeting the requirement from the commodity information associated with the media event; for example, at least one item of information having the greatest degree of association may be selected, the number of items of information to be selected may be specified or set in advance, or at least one item of information having a degree of association greater than a set degree of association threshold may be selected, and the degree of association threshold may be set in advance. When analyzing whether each commodity information is associated with the media event, matching may be performed according to the target attribute words corresponding to each commodity information and the media event, and whether the commodity information is associated with the media event is determined according to whether the matching degree is greater than a set second matching degree threshold, and the detailed process of this method may be described in the above-mentioned method b2, and is not described herein again.

After at least one commodity information with the correlation degree meeting the requirement is selected, commodity pattern information of the at least one commodity information can be generated according to the description information of the media event, the commodity pattern information is pattern information obtained by fusing with the media event, and the commodity pattern information is different from the traditional commodity description information. For example, the conventional commodity description information may include a commodity picture and a price, and the commodity pattern information after the media event is fused may include the commodity picture, the price and information related to the media event, such as information of "the commodity is a money burst due to the media event xxx", "the commodity is a money identical commodity in the media event xxx", and "the commodity is a money star due to the media event xxx", as shown in fig. 2b to fig. 2 d. Here, the "information related to the media event" is merely an exemplary illustration, and is not limited thereto, and any information form and style that can be related to the media event are applicable to the embodiment of the present application. It should be noted that the medialized store information of the target store further includes some traditional store information, such as the name of the store, the rating of the store, and the like.

In the aspect c2, the media event is merged with the dimension of the store to form the medialized store information. Optionally, the attribute information of the target store and the description information of the media event may be fused by using a preset first pattern template to generate store pattern information associated with the media event. Alternatively, the first paperwork template may be a case-by-case paperwork template in which a reason area and a result area are included, the reason area including information of the media event to be filled in and a preset reason paperwork, and the result area including information of the target store to be filled in and a preset result paperwork. For example, the preset reason document may be "bring by xxx" where xxx is the information of the media event to be filled; the preset results document may be "xxx flare," where xxx is the information of the target store to be filled. Or, the first document template may be a related document template, the first document template includes a first area, a second area and related words between the first area and the second area, the first area and the second area are areas to be filled, and are respectively used for filling information of the media event and information of the target store, filling positions of the two information are not limited, the information of the media event may be filled in the first area, the information of the target store may be filled in the second area, and vice versa. Alternatively, examples of associated-type document templates, such as "xxx associated xxx", "xxx appears in xxx", "xxx is a shop in xxx", etc.

In the aspect c3, the dimension of the commodity information is fused with the media event to form the medialized shop information. Optionally, in a case where the target store is associated with the media event, whether the commodity information in the target store is associated with the media event or not, at least one commodity information whose popularity information meets the requirement may be selected from the commodity information in the target store according to the popularity information of each commodity information in the target store; and then, fusing the dimension of the commodity information with the media event to form media store information, namely generating commodity file information of at least one commodity information according to the description information of the media event. The popularity information of each commodity information can be selected from the user behavior data and/or the store sales data of the target store in a specified period of time, and the user behavior data and/or the store sales data corresponding to the commodity information can be selected; and analyzing the popularity information of the commodity information according to the user behavior data and/or the shop sales data corresponding to the commodity information.

Further alternatively, in the above-mentioned modes c1 and c3, after the at least one item of merchandise information is selected, the merchandise document information of the at least one item of merchandise information may be generated according to the description information of the media event. Wherein, a mode of generating the commodity pattern information of at least one commodity information comprises: performing file translation on user behavior data and/or store sales data corresponding to at least one commodity information by using a preset second file template to obtain hotness file information corresponding to at least one commodity information; and supplementing the popularity pattern information corresponding to the at least one commodity information by taking the description information of the media event as popularity reason information so as to obtain the commodity pattern information of the at least one commodity information. In this embodiment, the second document template functions to perform document translation on the user behavior data and/or the store sales data corresponding to each commodity information, and for example, the user behavior data and/or the store sales data corresponding to the commodity information may be counted as the visit volume and/or the sales volume (or the volume of bargain) of the commodity information, and for the commodity information of which the visit volume exceeds the visit volume threshold and/or the sales volume exceeds the sales volume threshold, the user behavior data and/or the store sales data may be translated into document information such as "xxx commodity information exploded" or "xxx commodity information very popular".

And then, the description information of the media event is used as heat reason information, and heat pattern information corresponding to at least one commodity information is supplemented to obtain the commodity pattern information of the at least one commodity information. Alternatively, the description information of the media event may be directly supplemented as the popularity reason information to the popularity case information corresponding to the commodity information, so as to obtain the commodity case information of the commodity information, which includes the popularity reason information and the popularity case information. Or, with reference to the first document template, the result area corresponding to the hotness document information corresponding to the at least one item of merchandise information, the reason area corresponding to the description information of the media event, and the item document information of the at least one item of merchandise information are obtained by filling information into the first document template.

In an alternative embodiment, the targeted stores may be filtered or filtered in order to recommend a better targeted store to the user, taking into account that the number of media events occurring over a period of time may be greater, and accordingly, the number of targeted stores associated with the media events may be greater. Specifically, when the number of the target stores is larger than the set number threshold, the plurality of target stores can be filtered according to the attribute information of the media event associated with the target stores, the commodity preference information of the user and/or the attribute information of the target stores, so as to reduce the number of the target stores, and facilitate the user to find and visit interested stores from the target stores with proper number more quickly and conveniently.

In this embodiment, a specific embodiment of filtering a plurality of targeted stores according to the attribute information of the media event associated with the targeted store, the commodity preference information of the user, and/or the attribute information of the targeted store is not limited, and any filtering method capable of reducing the number of targeted stores is applicable to the embodiment of the present application. The filtration mode is exemplified below:

mode d1: and filtering the plurality of target stores according to the attribute information of the media events related to the target stores.

Mode d2: and filtering the plurality of target shops according to the commodity preference information of the user.

Mode d3: and filtering the plurality of target stores according to the attribute information of the target stores.

Mode d4: and filtering the plurality of target stores according to the attribute information of the media events related to the target stores and the commodity preference information of the users.

Mode d5: and filtering the plurality of target stores according to the attribute information of the media events related to the target stores and the attribute information of the target stores.

Mode d6: and filtering the plurality of target stores according to the attribute information of the target stores and the commodity preference information of the users.

Mode d7: and filtering the plurality of target stores according to the attribute information of the media events related to the target stores, the commodity preference information of the users and the attribute information of the target stores.

In each of the above-described embodiments, when filtering a plurality of target stores based on attribute information of media events associated with the target stores, target stores having low heat information of associated media events may be filtered based on heat information of media events associated with the stores, for example, target stores having heat information of associated media events smaller than a set heat threshold may be filtered, or target stores may be sorted in descending order of heat information of associated media events, and a number of target stores ranked last may be filtered.

In each of the above-described embodiments, when filtering a plurality of target stores based on the attribute information of the target stores, the target stores having low degree of popularity information may be filtered out based on the degree of popularity information of the target stores, for example, the target stores having degree of popularity information smaller than a set degree of popularity threshold may be filtered out, or the target stores may be sorted in descending order of degree of popularity information, and the target stores ranked last may be filtered out.

In each of the above-described embodiments, when filtering a plurality of target stores according to the commodity preference information of the user, the commodity information or the commodity category preferred by the user may be determined according to the commodity preference information of the user, and the preference degree of the user for each target store may be calculated according to the commodity information or the commodity category preferred by the user in combination with the attribute information of each target store; for example, target stores with a preference degree smaller than a set preference degree threshold are filtered, or target stores are sorted in the order of preference degrees from high to low, and a plurality of target stores with the last ranking are filtered.

After the medialized store information of the target store is generated, the medialized store information of the target store can be displayed on the first page so as to achieve the aim of recommending the target store to the user. Wherein the first page is a shop recommendation result page. In the present embodiment, the implementation form and the page layout of the first page are not limited, and any page form and layout capable of presenting the medialized store information of the target store to the user are applicable to the present embodiment.

In an alternative embodiment, the targeted store's medialized store information may be presented on a first page that includes a plurality of display regions, each carrying medialized information for a targeted store. One page layout of the first page is shown in fig. 2b, and the other page layout is shown in fig. 2 c. In FIG. 2b, with a streaming layout, the medialized store information for each targeted store is presented sequentially in card form on a first page; in fig. 2c, a multi-column layout is adopted, specifically including two columns, and the medialized store information of each target store is displayed on the first page in two columns in the form of a card.

In an alternative embodiment, in order to facilitate the user to quickly select a desired store from the targeted stores for access, the targeted stores may be displayed in a classified manner. Specifically, classification tags can be preset, and each classification tag corresponds to a class of stores, wherein the classification tags are not limited and can be flexibly set according to application requirements. Classifying the target stores based on preset classification labels and attribute information of the target stores to obtain at least one type of target stores; and then, displaying at least one type of target shop in a classified mode according to the classification result. Further optionally, at least one tab page may be embedded in the first page, where each tab page corresponds to one category tag for displaying the store information corresponding to the category tag. Based on the above, at least one type of target stores can be respectively displayed on at least one label page embedded in the first page. As shown in fig. 2d, the interface diagram of the first page including a plurality of tab pages is shown. In fig. 2d, N tab pages are shown, wherein tab pages 1 and 2 correspond to women's clothing and children's clothes, respectively, tab page N corresponds to kitchen ware, and currently, the target shop and its medialized shop information in tab page 1 are displayed.

Optionally, in order to further facilitate the user to quickly select a desired store from each type of target stores, before each type of target store is displayed on the corresponding tab, at least one type of target store may be sorted according to the commodity preference information of the user, the attribute information of the media event associated with the target store, and/or the attribute information of the target store. And then, respectively displaying at least one type of target stores on at least one label page embedded in the first page according to the sequencing result of the at least one type of target stores. For example, a target store with a higher user preference may be ranked ahead for preferential presentation to the user; the more popular targeted stores of the associated media event are ranked ahead for preferential presentation to the user and/or the targeted stores with a particular attribute (e.g., more popular) are ranked ahead for preferential presentation to the user.

Further, the first page shown in fig. 2 b-2 d further includes: a first jump control. The first jump control can be a text, a picture, an icon, a button, or the like, without limitation. The first jump control points to a second page, and the second page is a page for performing store recommendation in an aggregation mode, which may be referred to as a store aggregation page for short. In particular, when a user wishes to view a store on the store aggregation page, an access operation may be initiated through a first jump control. Based on this, as shown in fig. 2e, after step 204, the method of this embodiment further includes:

205. responding to the triggering operation of the first jumping control, jumping from the first page to a second page, and displaying a plurality of shop information which are displayed in an aggregation mode according to the first aggregation dimension and a second jumping control which is associated with the second aggregation dimension on the second page; and the second jump control points to a third page, and the third page comprises a plurality of shop information which are displayed in an aggregation mode according to the second aggregation dimension.

Further optionally, as shown in fig. 2e, after step 205, the method further includes:

206. and responding to the triggering operation of the second jumping control, jumping from the second page to a third page, wherein the third page displays a plurality of shop information which is displayed in a gathering mode according to a second aggregation dimension.

In this embodiment, the aggregation dimension for performing aggregation display on stores is not limited. And performing aggregation display on the stores, namely aggregating the stores with the same aggregation dimension together and displaying the stores on the same page.

In an alternative embodiment, the first and second aggregation dimensions may be the same type of aggregation dimension, but are different aggregation dimensions. In one scenario, stores may be aggregated in physical blocks, and the first and second aggregation dimensions may be different physical blocks. Preferably, the physical block corresponding to the first aggregation dimension may be determined according to a current location of the user, for example, the physical block closest to the current location of the user may be determined, and then store information of a store associated with the physical block is displayed on a second page in an aggregation manner, where the store associated with the physical block may be, for example, a physical store in the physical block, or a place where the store is located in the physical block. The physical blocks corresponding to the second aggregation dimension may be other physical blocks with higher awareness or relatively far distance from the current user.

In an alternative embodiment, the first and second aggregation dimensions may be different types of aggregation dimensions. In one scenario, the first aggregate dimension may be a physical block, the second aggregate dimension may be a store style or a home merchandising category, or the like.

In the above embodiments, the second polymerization dimension may be one or more. The second page or the third page has the same or similar page shape, and the shape of one of the second page or the third page is shown in fig. 2 f. Here, the plurality of stores displayed in the aggregate on the second page or the third page may include only stores associated with the media event, may include only stores not associated with the media event, and may include both stores associated with the media event and stores not associated with the media event, without limitation. In fig. 2f, for illustration purposes, the target store 1 is a store associated with a media event and the target store M is a store not associated with a media event, including both stores associated with media events and stores not associated with media events. Further, in order to improve the shape of the shop exhibition, the shop exhibition may be performed by a 3D stereoscopic method or an AR method, thereby enhancing the sense of substitution of shopping.

In the embodiment of the application, the media event is integrated into the store recommendation process, so that more stores related to the media event can be recommended to users, and the enrichment of store recommendation is realized; in addition, when the stores are recommended to the users, the media store information is displayed to the users, the attraction of the stores to the users is increased through the media store information, the probability of the users accessing the stores is improved, the efficiency of the users obtaining the needed commodities from the stores is improved, and the use experience of the users on the E-commerce applications is improved. Furthermore, the exposure rate and the user flow of the shop can be increased, and the sales data of the shop can be improved.

It should be noted that the embodiment shown in fig. 2a may be implemented in the terminal device alone, or may be implemented by the terminal device and the server device in cooperation. In the case where the embodiment shown in fig. 2a is implemented by cooperation of the terminal device and the server device, a shop information recommendation method is described from the perspective of the terminal device, and includes: the terminal equipment responds to trigger operation recommended by the shop and sends a shop recommendation request to the server equipment; receiving medialization information of a target shop returned by a server-side device, wherein the target shop is a shop which is determined by the server-side device according to user behavior data and/or shop sales data of at least one shop in a specified period and is associated with a media event, and the medialization shop information is generated according to the media event associated with the target shop; the media shop information of the target shop is displayed on the first page so as to recommend the target shop to the user.

Described from the perspective of a server device, a store information recommendation method includes: responding to a shop recommendation request sent by terminal equipment, and acquiring user behavior data and/or shop sales data of at least one shop in a specified period; acquiring a target shop of the associated media event from at least one shop according to the user behavior data and/or the shop sales data of the at least one shop in a specified period of time; generating medialized shop information of a target shop according to a media event related to the target shop, wherein the medialized shop information comprises information related to the media event; and transmitting the medialized shop information of the target shop to the terminal equipment so that the terminal equipment can display the medialized shop information of the target shop. For a detailed description of the related operations, reference may be made to the above embodiments, which are not repeated herein.

In addition to the above embodiments, the present application provides a store information recommendation method in which media events are merged into a store recommendation process from different dimensions. Specifically, as shown in fig. 3, the shop information recommendation method includes:

301. acquiring a target media event occurring in a specified time period;

302. determining a targeted store associated with the targeted media event from the at least one store;

303. generating medialized shop information of a target shop according to the target media event, wherein the medialized shop information comprises information related to the target media event;

304. the media shop information of the target shop is shown on the first page to recommend the target shop to the user.

In the embodiment, from the perspective of a media event, a target media event is obtained first, and then a target store associated with the target media event is determined; after the target store associated with the target media event is determined, the medialized store information of the target store can be generated according to the target media event, and the medialized store information of the target store is displayed on the first page, so that the purpose of recommending the target store to the user is achieved. For the manner of acquiring the media event, reference may be made to the foregoing embodiments, which are not described herein again.

In an optional embodiment, at least one media event occurring in a specified time period may be acquired, and from the at least one media event, a media event with a degree of heat greater than a first set degree of heat threshold and containing a target attribute word is selected as a target media event; the target attribute words comprise store attribute words and/or commodity attribute words.

In an alternative embodiment, determining a targeted store associated with a targeted media event from among at least one store comprises: extracting target attribute words from description information of the target media event, wherein the target attribute words comprise shop attribute words and/or commodity attribute words; for any store, matching attribute information of the store and/or commodity information in the store with a target attribute word; and selecting the target shop from the candidate shops by taking the shop of the matched target attribute word as the candidate shop. For a detailed implementation of extracting a target attribute word from the description information of the target media event and matching the attribute information of any store and/or the commodity information in any store with the target attribute word, reference may be made to the foregoing embodiment, and details are not repeated here.

After the candidate stores are selected, a target store may be selected from the candidate stores. Optionally, at least one store may be randomly selected from the candidate stores as the target store, or at least one store with a higher association degree may be selected from the candidate stores as the target store according to the association degree of the candidate store and the target media event; alternatively, the target store may be selected from at least one candidate store based on attribute information of the candidate store and/or product preference information of the user.

For a detailed implementation of generating the medialized store information of the target store according to the target media event and displaying the medialized store information of the target store on the first page, reference may be made to the foregoing embodiment, which is not described herein again.

Here, the shop information recommendation method shown in fig. 3 may be implemented by a terminal device and a server device in cooperation, or may be implemented by the terminal device alone.

In addition to the above embodiments, an embodiment of the present application provides a store information recommendation method, as shown in fig. 4, including:

401. responding to trigger operation recommended by a shop, and acquiring the current position of a user;

402. determining a first physical block according to the current position of a user;

403. acquiring a target shop associated with a first physical block;

404. store information for a targeted store associated with a first physical block is presented on a first page.

In this embodiment, store recommendations may be made to the users in a store aggregation manner according to the current location of the users. Specifically, the stores are aggregated by taking the physical blocks as aggregation dimensions, namely, a first physical block is determined according to the current position of the user, and then the store information of the target stores associated with the first physical block is displayed on a first page, so that the purpose of recommending the stores to the user in a store aggregation mode is achieved. The first physical block may be, but is not limited to, the physical block closest to the current location of the user. In the embodiments of the present application, the definition of the physical block is not limited, and the physical block may be any physical block, some business blocks or travel blocks with high awareness, or blocks where a shopping mall or a supermarket is located. The store associated with the first physical block may be an online store in which an entity store or a joint store exists in the first physical block, or an online store in which a pick-up point or an after-sales service station is provided in the first physical block.

In an optional embodiment, the first page further comprises: and at least one block switching control, wherein different block switching controls correspond to different other physical blocks. The method further comprises the following steps: and responding to the triggering operation of any block switching control, and jumping from a first page to a second page, wherein the second page contains shop information of target shops related to other physical blocks corresponding to any block switching control.

In an optional embodiment, the obtaining of the targeted store associated with the first physical block may include: acquiring at least one shop related to a first physical block according to block attribute information of shops on each line; a targeted store of associated media events is obtained from the at least one store based on user behavior data and/or store sales data for the at least one store over a specified period of time. For a detailed implementation of the target store for obtaining the associated media event from the at least one store according to the user behavior data and/or the store sales data of the at least one store in the designated period of time, reference may be made to the foregoing embodiment, which is not described herein again.

In the embodiment, the current position of the user is used as a basis, the stores are aggregated in the physical block, and then the stores which are close to the user and are associated with the same physical block are aggregated and recommended to the user, so that the online stores and the offline entity stores can be combined, and more convenient and comprehensive services can be provided for the user. For example, the user may order at an online store and pick up goods at the physical store, or the user may complete delivery nearby at the physical store, or the user may try on or make up or after-sales service at the physical store, or the user may go to the online physical store for other things.

It should be noted that the execution subjects of the steps of the methods provided in the above embodiments may be the same device, or different devices may be used as the execution subjects of the methods. For example, the execution subjects of step 201 to step 204 may be device a; for another example, the execution subject of steps 201 and 204 may be device a, and the execution subject of steps 202 and 203 may be device B; and so on.

In addition, in some of the flows described in the above embodiments and the drawings, a plurality of operations are included in a specific order, but it should be clearly understood that the operations may be executed out of the order presented herein or in parallel, and the sequence numbers of the operations, such as 401, 402, etc., are merely used to distinguish various operations, and the sequence numbers themselves do not represent any execution order. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.

Fig. 5a is a schematic structural diagram of a store information recommendation device according to an embodiment of the present application. As shown in fig. 5a, the apparatus comprises:

the data acquisition module 51a is used for responding to the trigger operation recommended by the shop and acquiring user behavior data and/or shop sales data of at least one shop in a specified period;

a store acquisition module 52a, configured to acquire a target store of the associated media event from the at least one store according to the user behavior data and/or the store sales data of the at least one store within a specified period of time;

an information generating module 53a for generating medialized store information of the target store according to the media event associated with the target store, the medialized store information including information associated with the media event;

the information presentation module 54a is configured to present the medialized store information of the target store on a first page to recommend the target store to the user.

In an alternative embodiment, the store acquisition module 52a is specifically configured to: generating visit rate information of at least one store according to user behavior data and/or store sales data of the at least one store in a specified period; selecting at least one candidate shop meeting the popularity condition from at least one shop according to the visit popularity information of the at least one shop; at least one candidate store is associated with a known media event from at least one information dimension to obtain a targeted store associated to the media event.

Further optionally, the store acquiring module 52a, when acquiring the target store associated to the media event, is specifically configured to: extracting target attribute words from description information of known media events, wherein the target attribute words comprise shop attribute words and/or commodity attribute words; for any candidate shop, matching the attribute information of any candidate shop and/or the commodity information in any candidate shop with the target attribute words; if the target attribute words are matched, any candidate store is taken as a target store associated with the media event.

In an alternative embodiment, as shown in fig. 5a, the store information recommending apparatus further includes: and a store filtering module 55a configured to filter the targeted stores according to the attribute information of the media events related to the targeted stores, the commodity preference information of the users and/or the attribute information of the targeted stores when the number of the targeted stores is greater than a set number threshold.

In an optional embodiment, the information generating module 53a is specifically configured to perform at least one of the following operations when generating the medialized store information of the targeted store according to the media event associated with the targeted store:

according to the association degree of each commodity information and the media event in the target store, selecting at least one commodity information with the association degree meeting the requirement, and generating commodity file information of the at least one commodity information as media store information according to the description information of the media event;

generating store file information related to the media event as media store information according to the attribute information of the target store and the description information of the media event;

and selecting at least one commodity information with the heat information meeting the requirement from the heat information of each commodity information in the target store, and generating the commodity file information of the at least one commodity information as the media store information according to the description information of the media event.

Further optionally, when the information generating module 53a generates the commodity file information of the at least one commodity information according to the description information of the media event, the information generating module is specifically configured to: performing file translation on user behavior data and/or shop sales data corresponding to at least one commodity information by using a preset file template to obtain hotness file information corresponding to at least one commodity information; and supplementing the popularity pattern information corresponding to the at least one commodity information by taking the description information of the media event as popularity reason information so as to obtain the commodity pattern information of the at least one commodity information.

In an optional embodiment, the information display module 54a is specifically configured to: classifying the target stores according to preset classification labels and attribute information of the target stores to obtain at least one type of target stores; sequencing at least one type of target stores respectively according to commodity preference information of users, attribute information of media events related to the target stores and/or attribute information of the target stores; and respectively displaying at least one type of target stores on at least one label page embedded in the first page according to the sequencing result of the at least one type of target stores, wherein one label page corresponds to one classification label.

In an alternative embodiment, the first page further includes a first jump control thereon, and as shown in fig. 5a, the store information recommending apparatus further includes: the page jump module 56a is configured to jump from a first page to a second page in response to a triggering operation of a first jump control, where the second page displays a plurality of store information displayed in an aggregated manner according to a first aggregation dimension, and a second jump control associated with a second aggregation dimension; and the second jump control points to a third page, and the third page comprises a plurality of shop information which are displayed in an aggregation mode according to the second aggregation dimension.

Further optionally, the page jump module 56a is further configured to: and responding to the triggering operation of the second jumping control, jumping from the second page to a third page, wherein a plurality of shop information which is displayed in an aggregation mode according to the second aggregation dimension is displayed on the third page.

The apparatus shown in fig. 5a can perform the method of the embodiment shown in fig. 2a, and the implementation principle and the technical effect are not described again. The specific manner in which each module of the apparatus shown in fig. 5a performs operations in the above-described embodiment has been described in detail in the embodiment related to the method, and will not be described in detail here.

Fig. 5b is a schematic structural diagram of another store information recommendation device according to an embodiment of the present application. As shown in fig. 5b, the apparatus comprises:

an event acquisition module 51b, configured to acquire a target media event occurring within a specified time period;

a store determination module 52b for determining a target store associated with the target media event from the at least one store;

an information generating module 53b for generating, based on the target media event, medialized store information of the target store, the medialized store information including information associated with the target media event;

and the information display module 54b is used for displaying the medialized shop information of the target shop on the first page so as to recommend the target shop to the user.

In an optional embodiment, the event obtaining module 51b is specifically configured to: selecting a media event with the heat degree larger than a first set heat degree threshold value and containing a target attribute word from at least one media event occurring in a specified time period as a target media event; the target attribute words include store attribute words and/or item attribute words.

In an alternative embodiment, the store determination module 52b is specifically configured to: extracting target attribute words from the description information of the target media event, wherein the target attribute words comprise shop attribute words and/or commodity attribute words; for any shop, matching the attribute information of any shop and/or the commodity information in any shop with the target attribute words; and selecting the target shop from the candidate shops by taking the shop of the matched target attribute word as the candidate shop.

The apparatus shown in fig. 5b can perform the method of the embodiment shown in fig. 3, and the implementation principle and the technical effect are not described in detail. The specific manner in which the respective modules of the apparatus shown in fig. 5b in the above-described embodiment perform operations has been described in detail in the embodiment related to the method, and will not be described in detail here.

Fig. 5c is a schematic structural diagram of another store information recommendation device according to an embodiment of the present application. As shown in fig. 5c, the apparatus comprises:

a position acquisition module 51c, configured to acquire a current position of the user in response to a trigger operation recommended by the store;

the block determining module 52c is configured to determine a first physical block according to the current location of the user;

a store acquisition module 53c for acquiring a target store associated with a first physical neighborhood;

and an information presentation module 54c for presenting the store information of the target store associated with the first physical block on a first page.

In an optional embodiment, the first page further comprises: and at least one block switching control, wherein different block switching controls correspond to different other physical blocks. As shown in fig. 5c, the apparatus further comprises: and the page skipping module 55c is used for responding to the triggering operation of any block switching control, skipping from the first page to the second page, wherein the second page contains the shop information of the target shop related to other physical blocks corresponding to any block switching control.

In an optional embodiment, the store acquisition module 53c is specifically configured to: acquiring at least one shop related to a first physical block according to block attribute information of shops on each line; a targeted store of associated media events is obtained from the at least one store based on user behavior data and/or store sales data for the at least one store over a specified period of time.

The apparatus shown in fig. 5c can perform the method of the embodiment shown in fig. 4, and the implementation principle and the technical effect are not described in detail. The specific manner in which the respective modules of the apparatus shown in fig. 5c in the above-described embodiment perform operations has been described in detail in the embodiment related to the method, and will not be described in detail here.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 6, the electronic apparatus includes: a memory 61 and a processor 62.

The memory 61 is used for storing computer programs and may be configured to store other various data to support operations on the electronic device. Examples of such data include instructions for any application or method operating on the electronic device, contact data, messages, pictures, videos, and so forth.

A processor 62, coupled to the memory 61, for executing computer programs in the memory 61 for: responding to the trigger operation recommended by the shop, and acquiring user behavior data and/or shop sales data of at least one shop in a specified period; acquiring a target store of the associated media event from at least one store according to the user behavior data and/or the store sales data of the at least one store in a specified period; generating medialized shop information of the target shop according to the media event related to the target shop, wherein the medialized shop information comprises information related to the media event; the media shop information of the target shop is shown on the first page to recommend the target shop to the user.

In an alternative embodiment, the processor 62, when obtaining a targeted store of associated media events from at least one store, is specifically configured to: generating visit heat information of at least one store according to user behavior data and/or store sales data of the at least one store in a specified period; selecting at least one candidate shop meeting the popularity condition from at least one shop according to the visit popularity information of the at least one shop; at least one candidate store is associated with a known media event from at least one information dimension to obtain a targeted store associated with the media event.

Further optionally, the processor 62, when obtaining the target store associated to the media event, is specifically configured to: extracting target attribute words from description information of known media events, wherein the target attribute words comprise shop attribute words and/or commodity attribute words; for any candidate shop, matching the attribute information of any candidate shop and/or the commodity information in any candidate shop with the target attribute words; if the target attribute words are matched, any candidate store is taken as a target store associated with the media event.

In an alternative embodiment, processor 62 is further configured to: and when the number of the target stores is larger than the set number threshold value, filtering the target stores according to the attribute information of the media events related to the target stores, the commodity preference information of the users and/or the attribute information of the target stores.

In an alternative embodiment, the processor 62 is specifically configured to perform at least one of the following operations in generating the medialized store information for the targeted store based on the media event associated with the targeted store:

according to the association degree of each commodity information and the media event in the target store, selecting at least one commodity information with the association degree meeting the requirement, and generating commodity file information of the at least one commodity information as media store information according to the description information of the media event;

generating store file information related to the media event as media store information according to the attribute information of the target store and the description information of the media event;

and selecting at least one commodity information with the heat information meeting the requirement from the heat information of each commodity information in the target store, and generating the commodity file information of the at least one commodity information as the media store information according to the description information of the media event.

Further optionally, when the processor 62 generates the commodity file information of the at least one commodity information according to the description information of the media event, it is specifically configured to: translating the user behavior data and/or the shop sales data corresponding to at least one commodity information by using a preset file template to obtain hotness file information corresponding to at least one commodity information; and supplementing the popularity pattern information corresponding to the at least one commodity information by taking the description information of the media event as popularity reason information so as to obtain the commodity pattern information of the at least one commodity information.

In an alternative embodiment, the processor 62, when presenting the media store information of the targeted store on the first page, is specifically configured to: classifying the target stores according to preset classification labels and attribute information of the target stores to obtain at least one type of target stores; sorting at least one type of target stores respectively according to commodity preference information of users, attribute information of media events related to the target stores and/or attribute information of the target stores; and respectively displaying at least one type of target stores on at least one label page embedded in the first page according to the sequencing result of the at least one type of target stores, wherein one label page corresponds to one classification label.

In an alternative embodiment, the first page further includes a first jump control thereon, and the processor 62 is further configured to: responding to the triggering operation of the first jumping control, jumping from the first page to a second page, wherein a plurality of shop information displayed in an aggregation mode according to the first aggregation dimension and a second jumping control associated with the second aggregation dimension are displayed on the second page; and the second jump control points to a third page, and the third page comprises a plurality of shop information which are displayed in an aggregation mode according to the second aggregation dimension.

Further optionally, the processor 62 is further configured to: and responding to the triggering operation of the second jumping control, jumping from the second page to a third page, wherein a plurality of shop information which is displayed in an aggregation mode according to the second aggregation dimension is displayed on the third page.

Further, as shown in fig. 6, the electronic device further includes: communication components 63, display 64, power components 65, audio components 66, and the like. Only some of the components are schematically shown in fig. 6, and the electronic device is not meant to include only the components shown in fig. 6. In addition, the components within the dashed line frame in fig. 6 are optional components, not necessary components, and may be determined according to the product form of the electronic device. The electronic device of this embodiment may be implemented as a terminal device such as a desktop computer, a notebook computer, a smart phone, or an IOT device, or may be a server device such as a conventional server, a cloud server, or a server array. If the electronic device of this embodiment is implemented as a terminal device such as a desktop computer, a notebook computer, a smart phone, etc., the electronic device may include components within a dashed line frame in fig. 6; if the electronic device of this embodiment is implemented as a server device such as a conventional server, a cloud server, or a server array, the components in the dashed box in fig. 6 may not be included.

An embodiment of the present application further provides an electronic device, where a structure of the electronic device is the same as or similar to that of the electronic device shown in fig. 6, and may be specifically implemented by referring to the structure of the electronic device shown in fig. 6, where differences between the electronic device provided in this embodiment and the electronic device in the embodiment shown in fig. 6 are mainly that: the functions performed by the processor to execute the computer programs stored in the memory are different. For the electronic device provided in this embodiment, the processor thereof executes the computer program stored in the memory, and is configured to: responding to trigger operation recommended by a shop, and sending a shop recommendation request to server equipment; receiving medialization information of a target shop returned by a server-side device, wherein the target shop is a shop which is determined by the server-side device according to user behavior data and/or shop sales data of at least one shop in a specified period and is associated with a media event, and the medialization shop information is generated according to the media event associated with the target shop; the media shop information of the target shop is shown on the first page to recommend the target shop to the user.

An embodiment of the present application further provides an electronic device, where a structure of the electronic device is the same as or similar to that of the electronic device shown in fig. 6, and may be specifically implemented by referring to the structure of the electronic device shown in fig. 6, where differences between the electronic device provided in this embodiment and the electronic device in the embodiment shown in fig. 6 are mainly that: the functions performed by the processor to execute the computer programs stored in the memory are different. For the electronic device provided in this embodiment, the processor thereof executes the computer program stored in the memory, and is configured to: responding to a shop recommendation request sent by terminal equipment, and acquiring user behavior data and/or shop sales data of at least one shop in a specified period; acquiring a target shop of the associated media event from at least one shop according to the user behavior data and/or the shop sales data of the at least one shop in a specified period of time; generating medialized shop information of a target shop according to a media event related to the target shop, wherein the medialized shop information comprises information related to the media event; and transmitting the medialized shop information of the target shop to the terminal equipment so that the terminal equipment can show the medialized shop information of the target shop.

The electronic device of the above embodiment of the present application may execute the method of the embodiment shown in fig. 2a, and details of implementation principles and technical effects are not described again, and the specific manner of the above related operations has been described in detail in the embodiment of the method, and will not be described in detail here.

An embodiment of the present application further provides an electronic device, where a structure of the electronic device is the same as or similar to that of the electronic device shown in fig. 6, and may be specifically implemented by referring to the structure of the electronic device shown in fig. 6, where differences between the electronic device provided in this embodiment and the electronic device in the embodiment shown in fig. 6 are mainly that: the functions performed by the processor to execute the computer programs stored in the memory are different. For the electronic device provided in this embodiment, the processor thereof executes the computer program stored in the memory, and is configured to: acquiring a target media event occurring in a specified time period; determining a targeted store associated with the targeted media event from the at least one store; generating medialized store information of a target store according to the target media event, wherein the medialized store information comprises information related to the target media event; the media shop information of the target shop is shown on the first page to recommend the target shop to the user.

In an optional embodiment, the processor is specifically configured to: selecting a media event with the heat degree larger than a first set heat degree threshold value and containing a target attribute word from at least one media event occurring in a specified time period as a target media event; the target attribute words include store attribute words and/or item attribute words.

In an optional embodiment, the processor is specifically configured to: extracting target attribute words from the description information of the target media event, wherein the target attribute words comprise shop attribute words and/or commodity attribute words; for any shop, matching the attribute information of any shop and/or the commodity information in any shop with the target attribute words; and selecting the target shop from the candidate shops by taking the shop of the matched target attribute word as the candidate shop.

The electronic device of this embodiment may execute the method of the embodiment shown in fig. 3, and the implementation principle and the technical effect are not described again, and the specific manner of the related operations described above has been described in detail in the embodiment related to the method, and will not be described in detail here.

An embodiment of the present application further provides an electronic device, where a structure of the electronic device is the same as or similar to that of the electronic device shown in fig. 6, and may be specifically implemented by referring to the structure of the electronic device shown in fig. 6, where differences between the electronic device provided in this embodiment and the electronic device in the embodiment shown in fig. 6 mainly lie in: the functions performed by the processor executing the computer programs stored in the memory are different. For the electronic device provided in this embodiment, the processor thereof executes the computer program stored in the memory, and is configured to: responding to trigger operation recommended by a shop, and acquiring the current position of a user; determining a first physical block according to the current position of a user; acquiring a target shop associated with a first physical block; store information of a target store associated with a first physical block is presented on a first page.

In an optional embodiment, the first page further includes: and at least one block switching control, wherein different block switching controls correspond to different other physical blocks. Based thereon, the processor is further configured to: and responding to the triggering operation of any block switching control, jumping from the first page to a second page, wherein the second page contains the shop information of target shops related to other physical blocks corresponding to any block switching control.

In an optional embodiment, the processor is specifically configured to: acquiring at least one shop related to a first physical block according to block attribute information of shops on each line; a targeted store of associated media events is obtained from the at least one store based on user behavior data and/or store sales data for the at least one store over a specified period of time.

The electronic device of this embodiment may execute the method of the embodiment shown in fig. 4, and the implementation principle and the technical effect are not described again, and the specific manner of the related operations described above has been described in detail in the embodiment related to the method, and will not be described in detail here.

Accordingly, the present application also provides a computer readable storage medium storing a computer program, which when executed by a processor, causes the processor to implement the steps in the method embodiments shown in fig. 2a, fig. 3 or fig. 4.

Accordingly, embodiments of the present application also provide a computer program product, which includes computer program/instructions, when executed by a processor, cause the processor to implement the steps in the method embodiments shown in fig. 2a, fig. 3 or fig. 4.

The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random-Access Memory (SRAM), electrically Erasable Programmable Read Only Memory (EEPROM), erasable Programmable Read Only Memory (EPROM), programmable Read Only Memory (PROM), read Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.

The communication component is configured to facilitate wired or wireless communication between the device in which the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as a WiFi, a 2G, 3G, 4G/LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared Data Association (IrDA) technology, ultra Wide Band (UWB) technology, blueTooth (BlueTooth, BT) technology, and other technologies.

The Display includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

The power supply assembly provides power for various components of the device in which the power supply assembly is located. The power components may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device in which the power component is located.

The audio component may be configured to output and/or input an audio signal. For example, the audio component includes a Microphone (MIC) configured to receive an external audio signal when the device in which the audio component is located is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in a memory or transmitted via a communication component. In some embodiments, the audio assembly further comprises a speaker for outputting audio signals.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, compact disk Read-Only Memory (CD-ROM), optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The Memory may include volatile Memory in a computer readable medium, random Access Memory (RAM), and/or nonvolatile Memory such as Read Only Memory (ROM) or flash Memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase-change Random Access Memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash Memory or other Memory technology, compact Disc read only Memory (CD-ROM), digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (15)

1. A shop information recommendation method is characterized by comprising the following steps:

responding to the trigger operation recommended by the shop, and acquiring user behavior data and/or shop sales data of at least one shop in a specified period;

acquiring a target shop of the associated media event from the at least one shop according to the user behavior data and/or the shop sales data of the at least one shop in a specified period of time;

generating medialized shop information of the target shop according to the media event related to the target shop, wherein the medialized shop information comprises information related to the media event;

displaying the medialized shop information of the target shop on a first page to recommend the target shop to a user.

2. The method of claim 1, wherein obtaining a targeted store of associated media events from the at least one store based on user behavior data and/or store sales data for the at least one store over a specified period of time comprises:

generating visit heat information of the at least one store according to user behavior data and/or store sales data of the at least one store in a specified period of time;

selecting at least one candidate shop meeting a popularity condition from the at least one shop according to the visit popularity information of the at least one shop;

the at least one candidate store is associated with a known media event from at least one information dimension to obtain a targeted store associated with the media event.

3. The method of claim 2, wherein associating the at least one candidate store with a known media event from at least one information dimension to obtain a targeted store associated with the media event comprises:

extracting target attribute words from description information of known media events, wherein the target attribute words comprise shop attribute words and/or commodity attribute words;

for any candidate shop, matching attribute information of the candidate shop and/or commodity information in the candidate shop with the target attribute word;

and if the target attribute words are matched, taking any candidate shop as a target shop related to the media event.

4. The method of claim 1, further comprising, prior to generating the targeted store's medialized store information based on the targeted store associated media event:

and when the number of the target stores is larger than a set number threshold value, filtering the target stores according to the attribute information of the media events related to the target stores, the commodity preference information of the users and/or the attribute information of the target stores.

5. The method of claim 1, wherein generating the targeted store's medialized store information based on the targeted store's associated media event comprises at least one of:

according to the association degree of each commodity information in the target store and the media event, selecting at least one commodity information with the association degree meeting the requirement, and generating commodity file information of the at least one commodity information as media store information according to the description information of the media event;

generating store file information associated with the media event as media store information according to the attribute information of the target store and the description information of the media event;

and selecting at least one commodity information with the heat information meeting the requirement from the heat information of each commodity information in the target store, and generating the commodity file information of the at least one commodity information as the media store information according to the description information of the media event.

6. The method of claim 5, wherein generating the commodity pattern information of the at least one commodity information according to the description information of the media event comprises:

performing file translation on user behavior data and/or shop sales data corresponding to the at least one commodity information by using a preset file template to obtain hotness file information corresponding to the at least one commodity information;

and supplementing the popularity pattern information corresponding to the at least one commodity information by taking the description information of the media event as popularity reason information so as to obtain the commodity pattern information of the at least one commodity information.

7. The method of claim 1, wherein presenting the targeted store's medialized store information on a first page to recommend the targeted store to a user comprises:

classifying the target stores according to preset classification labels and attribute information of the target stores to obtain at least one type of target stores;

sorting the at least one type of target stores respectively according to commodity preference information of users, attribute information of media events related to the target stores and/or attribute information of the target stores;

and respectively displaying the at least one type of target stores on at least one label page embedded in the first page according to the sequencing result of the at least one type of target stores, wherein one label page corresponds to one classification label.

8. The method in accordance with claim 1, further comprising a first jump control on the first page, the method further comprising:

responding to the triggering operation of the first jumping control, jumping from the first page to a second page, wherein the second page displays a plurality of shop information displayed in a gathering mode according to a first gathering dimension and a second jumping control related to a second gathering dimension;

and the second jump control points to a third page, and the third page comprises a plurality of shop information which are displayed in an aggregation manner according to the second aggregation dimension.

9. A shop information recommendation method is characterized by comprising the following steps:

acquiring a target media event occurring in a specified time period;

determining a targeted store associated with the targeted media event from at least one store;

generating medialized shop information of the target shop according to the target media event, wherein the medialized shop information comprises information related to the target media event;

displaying the medialized shop information of the target shop on a first page to recommend the target shop to a user.

10. The method of claim 9, wherein obtaining a target media event that occurs within a specified time period comprises:

selecting a media event with the heat degree larger than a first set heat degree threshold value and containing a target attribute word as a target media event from at least one media event occurring in a specified time period; the target attribute words comprise store attribute words and/or commodity attribute words.

11. The method of claim 9 or 10, wherein determining a targeted store associated with the targeted media event from at least one store comprises:

extracting target attribute words from the description information of the target media event, wherein the target attribute words comprise shop attribute words and/or commodity attribute words;

for any shop, matching attribute information of the shop and/or commodity information in the shop with the target attribute words;

and selecting the shop of the target attribute word in the matching as a candidate shop, and selecting a target shop from the candidate shops.

12. A shop information recommendation method is characterized by comprising the following steps:

responding to trigger operation recommended by a shop, and sending a shop recommendation request to the server equipment;

receiving medialization information of a target shop returned by the server-side equipment, wherein the target shop is a shop which is determined by the server-side equipment according to user behavior data and/or shop sales data of at least one shop in a specified period and is associated with a media event, and the medialization shop information is generated according to the media event associated with the target shop;

displaying the medialized shop information of the target shop on a first page to recommend the target shop to a user.

13. A shop information recommendation method is characterized by comprising the following steps:

responding to a shop recommendation request sent by terminal equipment, and acquiring user behavior data and/or shop sales data of at least one shop in a specified period;

acquiring a target shop of the associated media event from the at least one shop according to the user behavior data and/or the shop sales data of the at least one shop in a specified period of time;

generating medialized shop information of the target shop according to the media event related to the target shop, wherein the medialized shop information comprises information related to the media event;

and sending the medialized shop information of the target shop to the terminal equipment so that the terminal equipment can display the medialized shop information of the target shop.

14. An electronic device, comprising: a memory and a processor; the memory for storing a computer program; the processor, coupled with the memory, is configured to execute the computer program to implement the steps of the method of any one of claims 1-8, 9-11, 12 and 13.

15. A computer-readable storage medium having a computer program stored thereon, which, when being executed by a processor, causes the processor to carry out the steps of the method of any one of claims 1-8, 9-11, 12 and 13.

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