CN114119146A - Recommendation method and device, electronic equipment and readable storage medium - Google Patents
Recommendation method and device, electronic equipment and readable storage medium Download PDFInfo
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
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Abstract
The embodiment of the disclosure provides a recommendation method, a recommendation device, an electronic device and a readable storage medium, wherein the method comprises the following steps: determining an intention merchant of a user according to historical operating data of the user; determining candidate merchants matching the intended merchant; and generating and displaying recommendation information for the candidate merchants, wherein the preset area range of the candidate merchants comprises the target address of the user. Embodiments of the present disclosure may determine the intended merchants of the user based on historical operational data of the user, thereby querying matching candidate merchants based on the intended merchants; and then, under the condition that the preset area range of the candidate merchant contains the target address of the user, generating and displaying recommendation information for the candidate merchant, so that the user can directly perform ordering operation according to the recommendation information, information confusion caused by merchants similar to the intention merchant of the user in the recommendation information is avoided, and the accuracy of the recommendation information is improved.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer processing technologies, and in particular, to a recommendation method, an apparatus, an electronic device, and a readable storage medium.
Background
With the rapid development of informatization, information provided by the internet to users is increased explosively, the demands of users are increasing day by day, and how to enable users to timely and accurately acquire required information from massive information becomes a problem which needs to be solved urgently.
Taking a take-out platform as an example, a plurality of brand chain merchants exist, brand names, brand icons and main categories of the brand chain merchants are extremely similar, when a user searches for a wanted brand chain merchant, a large number of similar brand merchants often appear in a recommendation result, the user needs to self-identify the recommendation result, the accuracy of the recommendation result is low, and the user experience is poor.
Disclosure of Invention
Embodiments of the present disclosure provide a recommendation method, an apparatus, an electronic device, and a readable storage medium, which may improve accuracy of recommendation information.
According to a first aspect of embodiments of the present disclosure, there is provided a recommendation method, the method including:
determining an intention merchant of a user according to historical operating data of the user;
determining candidate merchants matching the intended merchant;
and generating and displaying recommendation information for the candidate merchants, wherein the preset area range of the candidate merchants comprises the target address of the user.
According to a second aspect of embodiments of the present disclosure, there is provided a recommendation apparatus, the apparatus including:
the intention merchant determining module is used for determining the intention merchant of the user according to the historical operation data of the user;
a candidate merchant determination module to determine candidate merchants that match the intended merchants;
and the first information recommendation module is used for generating and displaying recommendation information for the candidate merchants, and the preset area range of the candidate merchants comprises the target address of the user.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic apparatus including:
a processor, a memory and a computer program stored on said memory and executable on said processor, said processor implementing the aforementioned recommendation method when executing said program.
According to a fourth aspect of embodiments of the present disclosure, there is provided a readable storage medium, wherein instructions, when executed by a processor of an electronic device, enable the electronic device to perform the aforementioned recommendation method.
The embodiment of the disclosure provides a recommendation method, a recommendation device, an electronic device and a readable storage medium, wherein the method comprises the following steps: determining an intention merchant of a user according to historical operating data of the user; determining candidate merchants matching the intended merchant; and generating and displaying recommendation information for the candidate merchants, wherein the preset area range of the candidate merchants comprises the target address of the user.
Embodiments of the present disclosure may determine the intended merchants of the user based on historical operational data of the user, thereby querying matching candidate merchants based on the intended merchants; and then, under the condition that the preset area range of the candidate merchant contains the target address of the user, generating and displaying recommendation information for the candidate merchant, so that the user can directly perform ordering operation according to the recommendation information, information confusion caused by merchants similar to the intention merchant of the user in the recommendation information is avoided, and the accuracy of the recommendation information is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments of the present disclosure will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 illustrates an application scene architecture diagram in one embodiment of the present disclosure;
FIG. 2 shows a flow chart of the steps of a recommendation method in one embodiment of the present disclosure;
FIG. 3 shows a schematic structural diagram of a recommendation device in an embodiment of the present disclosure;
FIG. 4 shows a block diagram of an electronic device in an embodiment of the disclosure.
Detailed Description
Technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present disclosure, belong to the protection scope of the embodiments of the present disclosure.
Example one
Referring to fig. 1, an application scenario architecture diagram of a recommendation method provided by an embodiment of the present disclosure is shown. As shown in fig. 1, an application scenario of the embodiment of the present invention may include a terminal device 101 and a server 102. The terminal device 101 and the server 102 are connected through a wireless or wired network. The terminal device 101 includes, but is not limited to, an electronic device such as a mobile phone, a smart robot, an AI manual service, a mobile computer, and a tablet computer. The terminal device 101 may be installed with a client, where the client is a client used by a user, and the client may provide a search service, and the search content may include at least one of the following: gourmet, movies, hotels, travel, etc. The user may search for merchant information through the client.
The server 102 may be a server, a server cluster composed of several servers, or a cloud computing center. The terminal 101 and the server 102 may be used separately to execute the recommendation method provided in the embodiment of the present invention, and the terminal 101 and the server 102 may also be used to cooperatively execute the recommendation method provided in the embodiment of the present invention.
In one possible application scenario, a user interacts with the terminal device 101, and the terminal device 101 sends historical operation data of the user and a real-time search request to the server 102. The server 102 analyzes and processes the historical operation data sent by the terminal device 101, determines an intention merchant of the user, further determines a candidate merchant matched with the intention merchant, generates recommendation information for the candidate merchant and sends the recommendation information to the terminal device 101, and the terminal device 101 displays the received recommendation information.
In another possible application scenario, the terminal device 101 receives a real-time search request of a user through a client running in the terminal device, and obtains historical operation data of the user from the server 102, so as to determine an intended merchant of the user according to the historical operation data of the user; then, the client in the terminal device 101 queries a candidate merchant matching the intended merchant from a preset database, generates recommendation information for the candidate merchant, and displays the recommendation information.
It should be noted that the architecture diagram in the embodiment of the present invention is used to more clearly illustrate the technical solution in the embodiment of the present invention, and does not limit the technical solution provided in the embodiment of the present invention, and for other application scenario architectures and service applications, the technical method provided in the embodiment of the present invention is also applicable to similar problems.
The following describes a specific implementation process of the recommendation method provided by the present disclosure, with an example that the recommendation method provided by the present disclosure is executed by a client running in the terminal device 101. Referring to fig. 2, a flowchart illustrating steps of a recommendation method in an embodiment of the present disclosure is shown, specifically as follows:
It should be noted that the embodiment of the present disclosure is applicable to city delivery scenarios such as delivery, shopping, and the like for placing an instant order through a specific application platform (client). The instant order is an order which needs to be delivered immediately after a user places the order. In practice, take out orders are the most common instant orders. Of course, the disclosed embodiment does not limit the source of the instant order.
The historical operational data of the user includes operational data of the user with respect to historical instant orders, for example, the historical operational data may include historical search data, historical browsing data, historical review data, historical instant order information, and the like.
The intention trader is the corresponding mark of the trader which the user wants to search, and is used for reflecting the search requirement of the user.
The preset area range is used for limiting the distribution range of the merchant. For the merchants providing the city delivery service, the delivery distance is limited, and each merchant has a fixed delivery area. The merchant can provide the delivery service for the user only when the target address of the user, namely the real-time receiving address, does not exceed the preset area range of the merchant.
In the embodiment of the disclosure, an intention merchant of a user can be determined according to historical operation data of the user, then candidate merchants matched with the intention merchant are determined, and recommendation information for the candidate merchants is generated and displayed so that the user can select the candidate merchants and place an order. The recommendation method is used for the same-city distribution scene of the instant orders and requires a dealer to provide distribution service. Therefore, the determined preset area range of the candidate merchant needs to contain the target address of the user.
As an example, the merchants browsed by the user can be determined as the intention merchants according to the historical browsing data of the user at the client; or further, according to the browsing times of the user for each merchant in the past period of time, ranking the merchants browsed by the user, and determining the merchant with the higher browsing time as the intended merchant.
As another example, the merchant to which the user purchased the goods belongs, that is, the merchant that has placed the order, may be determined as the historical merchant according to the historical order information of the user. Further, in order to improve the reliability of the intended merchant, the merchant that the user has placed an order within a preset time, for example, within the past month, may be determined as the intended merchant; or counting the order placing times of the user at each merchant according to the historical order information of the user, and determining the merchants with the order placing times larger than a preset threshold value as the intention merchants.
As yet another example, merchants that the user has rated, and that are prone to positive ratings by the rating data, may be determined to be the intended merchants based on the user's historical rating data. Specifically, a machine learning model can be constructed based on a deep neural network, the machine learning model has an intention prediction capability, and the historical comment data of the user is analyzed through the machine learning model to predict whether the historical comment data of the user is positive evaluation or negative evaluation.
In addition, when the user initiates a real-time search request, the intention merchant of the user can be determined according to the historical operation data of the user and the real-time search request. For example, merchants corresponding to the user's historical operating data, merchants matching the user's real-time search request may be determined to be the intended merchants of the user, and so on.
It should be noted that in the embodiments of the present disclosure, merchant data, for example, information such as a merchant name, a merchant category, a merchant brand, a merchant address, and the like, may be stored in a service end of an application or platform providing instant delivery service, and after determining an intended merchant of a user, the service end may pass through the stored merchant data to a client, and the client determines a candidate merchant matching the intended merchant based on the merchant data; or the server side can determine candidate merchants matched with the intention merchants of the user according to merchant data stored in the server side, and the candidate merchants are transmitted to the client side, and the client side generates and displays recommendation information for the candidate merchants.
In an embodiment of the disclosure, the candidate merchant matching the intended merchant may be a merchant whose brand is consistent with the intended merchant of the user, and the preset area range of the candidate merchant includes the target address of the user. For example, the service end of the distribution platform may store a corresponding relationship between a merchant brand and a merchant name or a merchant ID, the service end may transmit the corresponding relationship between the merchant brand and the merchant name or the merchant ID to the client, the client determines, according to the corresponding relationship between the merchant brand and the merchant name or the merchant ID, a candidate merchant with a same merchant brand as an intended merchant, generates and displays recommendation information for the candidate merchant, and a user does not need to distinguish the merchant brand by himself, so that information confusion caused by the existence of similar merchant brands can be avoided, and accuracy of the recommendation information is improved.
The recommendation information may be a service page of the candidate merchant, or a recommendation information page including a service page jump entry of the candidate merchant, or a prompt identifier generated for the recommended merchant, for example, a prompt identifier such as "once placed at a same brand merchant" is generated and displayed in the service page of the recommended merchant, and so on. If at least two candidate merchants exist, the recommendation information can also be a list selection page of the at least two candidate merchants, and the list selection page can contain merchant profiles of the at least two candidate merchants and a skip entry of a service page, so that a user can select one of the candidate merchants, directly enter the service page of the candidate merchant through the skip entry of the service page of the candidate merchant, select a commodity and place an order.
According to the recommendation method provided by the embodiment of the disclosure, the intention merchants of the user can be determined based on the historical operation data of the user, then the candidate merchants matched with the intention merchants are determined, and the recommendation information for the candidate merchants is generated and displayed under the condition that the preset area range of the candidate merchants contains the target address of the user, so that the user can directly perform ordering operation according to the recommendation information, information confusion caused by other merchants similar to the intention merchants of the user in the recommendation information is avoided, and the accuracy of the recommendation information is improved.
In an optional embodiment of the present disclosure, the determining an intended merchant of the user according to the historical operation data of the user in step 201 includes:
step S11, determining a historical merchant identifier corresponding to the historical operation data of the user to obtain a first identifier set;
step S12, responding to the real-time search request of the user, determining the real-time merchant identification matched with the real-time search request, and obtaining a second identification set;
step S13, determining the intersection of the first identification set and the second identification set to obtain a third identification set;
step S14, querying merchants corresponding to the merchant identifiers in the third identifier set to obtain the intended merchants of the user.
The real-time search request of the user is a search request triggered by the user in real time in the search process. Optionally, the real-time search request comprises a search request triggered by an input operation of a search word and/or a search request triggered by a click operation of a historical search word or a historical order.
It should be noted that the merchant identifier in the present disclosure is used to indicate brand information of the merchant, for example, the merchant identifier may be a chain brand such as "bailing on the seabed", "liu shou", "every day", and the like. In order to further improve the accuracy of the recommendation information, the merchant identifier in the disclosure may further include category identifiers of merchants, such as "hot pot", "chuankai", "dessert", and so on, so as to avoid a situation that the recommended merchant brand is an intention brand of the user, but the merchant category is not the intention category of the user, due to the fact that the same brand corresponds to multiple categories.
It should be noted that different merchant identifiers may be set for different application scenarios. For example, for a take-out delivery scene, when a user searches for brand-linked merchants, a large number of similar brand merchants often appear in recommendation results generated by the existing recommendation method, and the user is required to discriminate the recommendation results by himself, so that the accuracy of recommendation information is low. To solve this problem, the recommendation method of the present disclosure may be adopted, and the merchant identifier is set according to brand information of the merchant providing the takeout service.
In the embodiment of the disclosure, when determining the intention merchant identifier corresponding to the real-time search request of the user according to the historical operation data of the user, the first identifier set may be determined according to the historical operation data of the user. The first identification set comprises historical merchant identifications corresponding to historical operation data of the user. Specifically, the historical merchant identifier may be a merchant identifier in a search result corresponding to the historical search operation of the user, or may be a merchant identifier corresponding to a merchant with a higher browsing frequency in browsing information corresponding to the historical browsing operation of the user, or may be a merchant identifier corresponding to a historical order of the user, and so on.
After receiving the real-time search request of the user, the real-time merchant identifier matched with the real-time search request of the user can be determined in response to the real-time search request of the user, and a second identifier set is obtained. Specifically, whether a search word corresponding to the real-time search request of the user can reflect brand information of a merchant or not can be judged, and if the search word can reflect the brand information of the merchant, a real-time merchant identifier corresponding to the real-time search request can be determined according to the brand information of the merchant corresponding to the search word. If the search term can not reflect the brand information of the merchants, determining a search result corresponding to the search term, and determining merchant identifications corresponding to the merchants in the search result as real-time merchant identifications corresponding to the real-time search request.
And after the first identification set and the second identification set are obtained, the intersection of the two sets is taken to obtain a third identification set. In this embodiment, the merchant corresponding to the merchant identifier in the third set of identifiers may be regarded as the intended merchant of the user.
In an optional embodiment of the disclosure, the method further comprises:
step S21, setting a prompt identifier for the candidate merchant, wherein the prompt identifier is used for reminding a user that the merchant identifier of the candidate merchant is consistent with the merchant identifier contained in the historical order;
and step S22, displaying the prompt identification in the display interface of the candidate merchant.
In an embodiment of the present disclosure, a prompt identifier may be added for the candidate merchant and displayed in a display interface of the candidate merchant, for example, the prompt identifier may be "placed an order once at the same brand merchant", and is used to prompt the user that the candidate merchant is the same brand merchant as the merchant included in the historical order.
In an optional embodiment of the present disclosure, the determining 202 a candidate merchant that matches the intended merchant designation comprises:
step S31, determining a first merchant identification corresponding to the intended merchant;
step S32, inquiring whether a second merchant identification matched with the first merchant identification exists in a preset merchant database;
step S33, if a second merchant identifier matched with the first merchant identifier exists, judging whether a preset area range of a merchant corresponding to the second merchant identifier contains the target address of the user;
step S34, if the preset area range of the merchant corresponding to the second merchant identifier includes the target address of the user, determining that the merchant corresponding to the second merchant identifier is a recommended merchant matching the intended merchant.
In the embodiment of the disclosure, the corresponding relationship between the merchants and the merchant identifiers may be stored in the merchant database in advance, so that after the intended merchant of the user is determined, whether a second merchant identifier matched with the first merchant identifier exists may be directly queried in the merchant database according to a first merchant identifier of the intended merchant, a merchant brand corresponding to the second merchant identifier is consistent with a merchant brand corresponding to the first merchant identifier, and information such as a merchant address and a merchant ID corresponding to the first merchant identifier may be different from information such as a merchant address and a merchant ID corresponding to the second merchant identifier. If a second merchant identifier matched with the first merchant identifier exists, a merchant corresponding to the second merchant identifier can be directly inquired in a merchant database, whether the preset area range of the merchant contains the target address of the user or not is judged, and if the preset area range of the merchant contains the target address of the user, the merchant can be determined to be a candidate merchant matched with the intention merchant of the user.
And determining candidate merchants matched with the intention merchants of the user according to the corresponding relation between the merchant identification and the merchants, so that the data query efficiency can be improved.
In an optional embodiment of the present disclosure, the determining, in step S33, whether the preset area range of the merchant corresponding to the second merchant identifier includes the destination address of the user includes:
step S331, obtaining the position coordinate and the distribution distance of the merchant corresponding to the second merchant identifier;
substep S332, determining a preset area range of the merchant according to the position coordinates and the distribution distance of the merchant;
substep S333, determining a target position coordinate according to the target address of the user;
substep S334, determining whether the preset area range of the merchant contains the target address of the user;
and a substep S335, if the preset area range of the merchant contains the target position coordinate, determining that the preset area range of the merchant contains the target address of the user.
In an embodiment of the disclosure, after determining the merchant corresponding to the second merchant identifier matching the first merchant identifier of the intended merchant, it may be further determined whether the target address of the user exceeds a preset area range of the merchant. Specifically, the position coordinate and the distribution distance of the merchant corresponding to the second merchant identifier may be obtained first, and the preset area range of the merchant is determined according to the position coordinate and the distribution distance of the merchant. The distribution distance is generally the radius of a preset area range of a merchant, and the merchant provides distribution service for a user with a target address in the preset area range by taking the position coordinate of the merchant as the center and the distribution distance as the radius. And then, determining a target position coordinate corresponding to the target address according to the target address of the user. It should be noted that the location coordinates of the merchant and the target location coordinates of the user may be longitude and latitude coordinates, so that it can be determined that the preset area range of the merchant includes the target address of the user according to whether the target location coordinates fall within the preset area range.
In an optional embodiment of the disclosure, the method further comprises:
step S41, if no candidate merchant matched with the intention merchant exists, determining a to-be-recommended merchant identifier related to the first merchant identifier of the intention merchant;
step S42, inquiring the merchant to be recommended matched with the merchant to be recommended identification;
and step S43, generating and displaying recommendation information for the merchants to be recommended under the condition that the target address of the user is determined not to exceed the preset area range of the merchants to be recommended.
The candidate merchant matching with the intention merchant of the user does not exist, namely the candidate merchant matching with the first merchant identification of the intention merchant does not exist, or the target address of the user exceeds the preset area range of the candidate merchant. In both cases, the user cannot obtain the delivery service of the merchant matching the intended merchant, and in order not to affect the user's use of the city delivery service, such as take-away delivery, the identity of the merchant to be recommended that is associated with the first merchant identity of the intended merchant may be determined. As an example, a merchant identifier similar to the first merchant identifier may be determined according to cosine similarity by calculating cosine similarity between the first merchant identifier and each merchant identifier pre-stored in a merchant database. For example, the merchant identifier whose cosine similarity with the first merchant identifier is greater than a preset threshold may be determined as the merchant identifier to be recommended, which is related to the first merchant identifier. And then inquiring the merchant to be recommended matched with the merchant to be recommended, and generating and displaying recommendation information for the merchant to be recommended under the condition that the target address of the user is determined not to exceed the preset area range of the merchant to be recommended. The processing procedure for the to-be-recommended merchant may refer to the processing procedure for the candidate merchant, which is not further described herein.
In an optional embodiment of the disclosure, the method further comprises:
step S51, if there is no second merchant identification matching the first merchant identification of the intended merchant, determining that there is no candidate merchant matching the intended merchant; alternatively, the first and second electrodes may be,
step S52, if there is a second merchant identifier matching the first merchant identifier of the intended merchant, and the preset area range of the merchant corresponding to the second merchant identifier does not include the target address of the user, determining that there is no candidate merchant matching the intended merchant.
In the embodiment of the disclosure, whether a candidate merchant matching the intended merchant exists may be determined by determining whether a second merchant identifier matching the first merchant identifier of the intended merchant exists and determining whether a preset area range of a merchant corresponding to the second merchant identifier includes the target address of the user. Specifically, if there is no second merchant identifier matching the first merchant identifier of the intended merchant, or there is a second merchant identifier matching the first merchant identifier of the intended merchant, but the preset area range of the merchant corresponding to the second merchant identifier does not include the target address of the user, it is determined that there is no candidate merchant matching the intention of the merchant.
In summary, the embodiments of the present disclosure provide a recommendation method, which may determine an intended merchant of a user based on historical operation data of the user, then determine a candidate merchant matched with the intended merchant, and generate and display recommendation information for the candidate merchant when a preset area range of the candidate merchant includes a target address of the user, so that the user may directly perform an order placing operation according to the recommendation information, thereby avoiding information confusion caused by other merchants similar to the intended merchant of the user in the recommendation information, and improving accuracy of the recommendation information.
Example two
Referring to fig. 3, a block diagram of a recommendation device in an embodiment of the present disclosure is shown, specifically as follows:
an intended merchant determination module 301 for determining an intended merchant of a user based on historical operating data of the user;
a candidate merchant determination module 302 for determining candidate merchants that match the intended merchants;
the first information recommending module 303 is configured to generate and display recommendation information for the candidate merchants, where a preset area range of the candidate merchants includes the target address of the user.
Optionally, the intent merchant determination module includes:
the first identification set determining submodule is used for determining historical merchant identifications corresponding to the historical operation data of the user to obtain a first identification set;
the second identification set determining submodule is used for responding to the real-time search request of the user, determining the real-time merchant identification matched with the real-time search request and obtaining a second identification set;
a third identifier set determining submodule, configured to determine an intersection of the first identifier set and the second identifier set, to obtain a third identifier set;
an intention merchant inquiry submodule, configured to inquire merchants corresponding to the merchant identifiers in the third identifier set to obtain the intention merchants of the user
Optionally, the real-time search request comprises a search request triggered by an input operation of a search word and/or a search request triggered by a click operation of a historical search word or a historical order.
Optionally, the apparatus further comprises:
the prompt identifier setting module is used for setting a prompt identifier for the candidate merchant, and the prompt identifier is used for reminding the user that the merchant identifier of the candidate merchant is consistent with the merchant identifier contained in the historical order;
and the prompt identifier display module is used for displaying the prompt identifier in a display interface of the candidate merchant.
Optionally, the candidate merchant determination module includes:
a first merchant identifier determining submodule for determining a first merchant identifier corresponding to the intended merchant;
the second merchant identifier determining submodule is used for inquiring whether a second merchant identifier matched with the first merchant identifier exists in a preset merchant database;
the preset area range judgment sub-module is used for judging whether the preset area range of the merchant corresponding to the second merchant identifier contains the target address of the user or not if the second merchant identifier matched with the first merchant identifier exists;
and the recommended merchant determining submodule is used for determining that the merchant corresponding to the second merchant identifier is the recommended merchant matched with the intention merchant if the preset area range of the merchant corresponding to the second merchant identifier contains the target address of the user.
Optionally, the preset region range determining sub-module includes:
the merchant position information acquisition unit is used for acquiring the position coordinates and the distribution distance of the merchant corresponding to the second merchant identifier;
the preset area range determining unit is used for determining the preset area range of the merchant according to the position coordinates and the distribution distance of the merchant;
the target position coordinate determination unit is used for determining the target position coordinate according to the target address of the user;
the preset area range judging unit is used for judging whether the preset area range of the merchant contains the target address of the user or not;
and the recommendation condition determining unit is used for determining that the preset area range of the candidate merchant contains the target address of the user if the preset area range of the candidate merchant contains the target position coordinates.
Optionally, the apparatus further comprises:
the to-be-recommended merchant identifier determining module is used for determining a to-be-recommended merchant identifier related to the first merchant identifier of the intention merchant if the candidate merchant matched with the intention merchant does not exist;
the to-be-recommended merchant query module is used for querying the to-be-recommended merchants matched with the to-be-recommended merchant identifications;
and the second information recommendation module is used for generating and displaying recommendation information for the merchants to be recommended under the condition that the target address of the user is determined not to exceed the preset area range of the merchants to be recommended.
Optionally, the apparatus further comprises:
the first judgment module is used for determining that no candidate merchant matched with the intention merchant exists if no second merchant identifier matched with the first merchant identifier of the intention merchant exists; alternatively, the first and second electrodes may be,
and the second judgment module is used for determining that no candidate merchant matched with the intention merchant exists if a second merchant identifier matched with the first merchant identifier of the intention merchant exists and the preset area range of the merchant corresponding to the second merchant identifier does not contain the target address of the user.
In summary, the embodiments of the present disclosure provide a recommendation apparatus, which may determine an intended merchant of a user based on historical operation data of the user, then determine a candidate merchant matching the intended merchant, and generate and display recommendation information for the candidate merchant when a preset area range of the candidate merchant includes a target address of the user, so that the user may directly perform an order placing operation according to the recommendation information, thereby avoiding information confusion caused by other merchants similar to the intended merchant of the user in the recommendation information, and improving accuracy of the recommendation information.
The second embodiment is an embodiment of the apparatus corresponding to the first embodiment, and the detailed description may refer to the first embodiment, which is not repeated herein.
An embodiment of the present disclosure also provides an electronic device, referring to fig. 4, including: a processor 401, a memory 402 and a computer program 4021 stored on said memory 402 and executable on said processor, said processor 401 implementing the recommendation method of the previous embodiment when executing said program.
Embodiments of the present disclosure also provide a readable storage medium, in which instructions, when executed by a processor of an electronic device, enable the electronic device to perform the recommendation method of the foregoing embodiments.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present disclosure are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the embodiments of the present disclosure as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the embodiments of the present disclosure.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the disclosure, various features of the embodiments of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, claimed embodiments of the disclosure require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of an embodiment of this disclosure.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
The various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a document processing apparatus according to embodiments of the present disclosure. Embodiments of the present disclosure may also be implemented as an apparatus or device program for performing a portion or all of the methods described herein. Such programs implementing embodiments of the present disclosure may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit embodiments of the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above description is only for the purpose of illustrating the preferred embodiments of the present disclosure and is not to be construed as limiting the embodiments of the present disclosure, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the embodiments of the present disclosure are intended to be included within the scope of the embodiments of the present disclosure.
The above description is only a specific implementation of the embodiments of the present disclosure, but the scope of the embodiments of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present disclosure, and all the changes or substitutions should be covered by the scope of the embodiments of the present disclosure. Therefore, the protection scope of the embodiments of the present disclosure shall be subject to the protection scope of the claims.
Claims (18)
1. A recommendation method, characterized in that the method comprises:
determining an intention merchant of a user according to historical operating data of the user;
determining candidate merchants matching the intended merchant;
and generating and displaying recommendation information for the candidate merchants, wherein the preset area range of the candidate merchants comprises the target address of the user.
2. The method of claim 1, wherein determining the intended merchant of the user based on historical operational data of the user comprises:
determining a historical merchant identifier corresponding to the historical operation data of the user to obtain a first identifier set;
responding to the real-time search request of the user, determining real-time merchant identifications matched with the real-time search request, and obtaining a second identification set;
determining an intersection of the first identifier set and the second identifier set to obtain a third identifier set;
and inquiring merchants corresponding to the merchant identifications in the third identification set to obtain the intention merchants of the user.
3. The method according to claim 2, wherein the real-time search request comprises a search request triggered by an input operation for a search word and/or a search request triggered by a click operation for a historical search word or a historical order.
4. The method of claim 1, further comprising:
setting a prompt identifier for the candidate merchant, wherein the prompt identifier is used for reminding a user that the merchant identifier of the candidate merchant is consistent with the merchant identifier contained in the historical order;
and displaying the prompt identification in a display interface of the candidate merchant.
5. The method of claim 1, wherein the determining candidate merchants that match the intended merchant comprises:
determining a first merchant identification corresponding to the intended merchant;
inquiring whether a second merchant identifier matched with the first merchant identifier exists in a preset merchant database;
if a second merchant identifier matched with the first merchant identifier exists, judging whether a preset area range of a merchant corresponding to the second merchant identifier contains the target address of the user;
and if the preset area range of the merchant corresponding to the second merchant identifier contains the target address of the user, determining that the merchant corresponding to the second merchant identifier is a recommended merchant matched with the intention merchant.
6. The method of claim 5, wherein the determining whether the preset area range of the merchant corresponding to the second merchant identifier contains the destination address of the user comprises:
acquiring the position coordinates and the distribution distance of the merchant corresponding to the second merchant identifier;
determining a preset area range of the merchant according to the position coordinates and the distribution distance of the merchant;
determining a target position coordinate according to the target address of the user;
judging whether the preset area range of the merchant contains the target address of the user or not;
and if the preset area range of the merchant contains the target position coordinates, determining that the preset area range of the merchant contains the target address of the user.
7. The method of claim 1, further comprising:
if no candidate merchant matched with the intention merchant exists, determining a to-be-recommended merchant identifier related to the first merchant identifier of the intention merchant;
inquiring the merchant to be recommended matched with the merchant to be recommended identification;
and under the condition that the target address of the user is determined not to exceed the preset area range of the business to be recommended, generating and displaying recommendation information for the business to be recommended.
8. The method of claim 7, further comprising:
determining that there is no candidate merchant matching the intended merchant if there is no second merchant identification matching the first merchant identification of the intended merchant; alternatively, the first and second electrodes may be,
and if a second merchant identifier matched with the first merchant identifier of the intention merchant exists, and the preset area range of the merchant corresponding to the second merchant identifier does not contain the target address of the user, determining that no candidate merchant matched with the intention merchant exists.
9. A recommendation device, characterized in that the device comprises:
the intention merchant determining module is used for determining the intention merchant of the user according to the historical operation data of the user;
a candidate merchant determination module to determine candidate merchants that match the intended merchants;
and the first information recommendation module is used for generating and displaying recommendation information for the candidate merchants, and the preset area range of the candidate merchants comprises the target address of the user.
10. The apparatus of claim 9, wherein the intent merchant determination module comprises:
the first identification set determining submodule is used for determining historical merchant identifications corresponding to the historical operation data of the user to obtain a first identification set;
the second identification set determining submodule is used for responding to the real-time search request of the user, determining the real-time merchant identification matched with the real-time search request and obtaining a second identification set;
a third identifier set determining submodule, configured to determine an intersection of the first identifier set and the second identifier set, to obtain a third identifier set;
and the intention merchant inquiry submodule is used for inquiring merchants corresponding to the merchant identifications in the third identification set to obtain the intention merchants of the user.
11. The apparatus according to claim 10, wherein the real-time search request comprises a search request triggered by an input operation for a search word and/or a search request triggered by a click operation for a historical search word or a historical order.
12. The apparatus of claim 9, further comprising:
the prompt identifier setting module is used for setting a prompt identifier for the candidate merchant, and the prompt identifier is used for reminding the user that the merchant identifier of the candidate merchant is consistent with the merchant identifier contained in the historical order;
and the prompt identifier display module is used for displaying the prompt identifier in a display interface of the candidate merchant.
13. The apparatus of claim 9, wherein the candidate merchant determination module comprises:
a first merchant identifier determining submodule for determining a first merchant identifier corresponding to the intended merchant;
the second merchant identifier determining submodule is used for inquiring whether a second merchant identifier matched with the first merchant identifier exists in a preset merchant database;
the preset area range judgment sub-module is used for judging whether the preset area range of the merchant corresponding to the second merchant identifier contains the target address of the user or not if the second merchant identifier matched with the first merchant identifier exists;
and the recommended merchant determining submodule is used for determining that the merchant corresponding to the second merchant identifier is the recommended merchant matched with the intention merchant if the preset area range of the merchant corresponding to the second merchant identifier contains the target address of the user.
14. The apparatus of claim 13, wherein the preset region range determining sub-module comprises:
the merchant position information acquisition unit is used for acquiring the position coordinates and the distribution distance of the merchant corresponding to the second merchant identifier;
the preset area range determining unit is used for determining the preset area range of the merchant according to the position coordinates and the distribution distance of the merchant;
the target position coordinate determination unit is used for determining a target position coordinate according to the target address of the user;
the preset area range judging unit is used for judging whether the preset area range of the merchant contains the target address of the user or not;
and the recommendation condition determining unit is used for determining that the preset area range of the candidate merchant contains the target address of the user if the preset area range of the candidate merchant contains the target position coordinates.
15. The apparatus of claim 9, further comprising:
the to-be-recommended merchant identifier determining module is used for determining a to-be-recommended merchant identifier related to the first merchant identifier of the intention merchant if the candidate merchant matched with the intention merchant does not exist;
the to-be-recommended merchant query module is used for querying the to-be-recommended merchants matched with the to-be-recommended merchant identifications;
and the second information recommendation module is used for generating and displaying recommendation information for the merchants to be recommended under the condition that the target address of the user is determined not to exceed the preset area range of the merchants to be recommended.
16. The apparatus of claim 15, further comprising:
the first judgment module is used for determining that no candidate merchant matched with the intention merchant exists if no second merchant identifier matched with the first merchant identifier of the intention merchant exists; alternatively, the first and second electrodes may be,
and the second judgment module is used for determining that no candidate merchant matched with the intention merchant exists if a second merchant identifier matched with the first merchant identifier of the intention merchant exists and the preset area range of the merchant corresponding to the second merchant identifier does not contain the target address of the user.
17. An electronic device, comprising:
processor, memory and computer program stored on the memory and executable on the processor, characterized in that the processor implements the recommendation method according to any of claims 1 to 8 when executing the program.
18. A readable storage medium, characterized in that instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the recommendation method of any one of method claims 1 to 8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114387040A (en) * | 2022-03-22 | 2022-04-22 | 触电网络科技(深圳)有限公司 | Intelligent customer-expanding method and system based on artificial intelligence |
| WO2023241430A1 (en) * | 2022-06-15 | 2023-12-21 | 李宝忠 | Electronic-commerce data processing method, and electronic device |
| CN117391821A (en) * | 2023-12-11 | 2024-01-12 | 南京中廷网络信息技术有限公司 | E-commerce platform information data standardization processing method and system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114387040A (en) * | 2022-03-22 | 2022-04-22 | 触电网络科技(深圳)有限公司 | Intelligent customer-expanding method and system based on artificial intelligence |
| WO2023241430A1 (en) * | 2022-06-15 | 2023-12-21 | 李宝忠 | Electronic-commerce data processing method, and electronic device |
| CN117391821A (en) * | 2023-12-11 | 2024-01-12 | 南京中廷网络信息技术有限公司 | E-commerce platform information data standardization processing method and system |
| CN117391821B (en) * | 2023-12-11 | 2024-03-08 | 南京中廷网络信息技术有限公司 | E-commerce platform information data standardization processing method and system |
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