CN112288464A - Commodity recommendation method and device, computer equipment and storage medium - Google Patents
Commodity recommendation method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a commodity recommendation method, a commodity recommendation device, computer equipment and a storage medium, wherein the commodity recommendation method comprises the following steps: acquiring user information; acquiring user history data based on the user information, wherein the user history data comprises commodity browsing records; acquiring regional commodity sales data; and recommending commodities based on the user history data and the regional commodity sales data. According to the commodity recommendation method, the commodity recommendation device, the computer equipment and the storage medium, the commodities are recommended based on the regional commodity sales data and the commodity browsing records of the user, the recommendation condition is adjusted in real time according to the preference difference and the sales condition change of the user, personalized commodity recommendation service can be provided for the user, and the recommendation effect is better.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for recommending a commodity, a computer device, and a storage medium.
Background
Under the condition that the public tends to shop through the internet, the personalized recommendation technology of the articles can help people to be in contact with the contents which are small enough and suitable for the people, and the information acquisition modes of people are diversified nowadays when the internet is popularized. In the process of personalized recommendation of people, the hot word recommendation occupies a larger share, the hot word optimization can directly improve the overall flow of the system, improve the client experience, the residence time of a single client and other indexes, and the client price (the average price consumed by each client) is also improved (needing to be matched with other commercialization modes). The commercial change ability will be improved when the user's engagement (depth and duration of engagement) is increased, which is the basis for creating a good business cycle.
Most of the traditional commodity recommendation methods recommend N commodities with the highest current operation rate obtained through real-time statistics to all users, cannot respond to different preferences of each user, has no difference between regions and client preferences, cannot follow the commodity browsing records of the users and timely change of sales conditions, and has poor recommendation effect, so that the use interest of the users is reduced, and the viscosity of the users is further reduced.
Disclosure of Invention
The embodiment of the application provides a commodity recommendation method, a commodity recommendation device, computer equipment and a storage medium, and aims to at least solve the problems that in the related technology, the traditional commodity recommendation method cannot respond to different preferences of each user, has no difference of regions and client preferences, cannot follow the commodity browsing records and the sales conditions of the users to change in time, and is poor in recommendation effect.
In a first aspect, an embodiment of the present application provides a commodity recommendation method, including:
acquiring user information;
acquiring user history data based on the user information, wherein the user history data comprises commodity browsing records;
acquiring regional commodity sales data;
and recommending commodities based on the user history data and the regional commodity sales data.
In some embodiments, the obtaining user information includes:
and acquiring a user identity identifier and a user equipment identifier.
In some embodiments, the obtaining user history data based on the user information further comprises:
determining a user status based on the user identity;
if the user state is a first state, acquiring the user historical data based on the user equipment identification;
and if the user state is a second state, acquiring the user historical data based on the user identity.
In some embodiments, the obtaining user history data based on the user information comprises:
and acquiring a commodity browsing record of the user corresponding to the user information in a preset time period, and taking the commodity browsing record as user history data.
In some embodiments, the recommending merchandise based on the user history data and regional merchandise sales data comprises:
acquiring a first commodity set according to the commodity browsing records;
acquiring a second commodity set according to the regional commodity sales data;
acquiring a commodity intersection of the first commodity set and the second commodity set;
sorting the commodities in the commodity intersection based on the browsed duration and the sales quantity of the commodities, and selecting a preset quantity of recommended commodity subclasses from high to low based on the sorting;
and sorting the commodities in the recommended commodity subclass based on the browsed duration and the sales quantity of the commodities, selecting a preset quantity of recommended commodities from high to low based on the sorting, and recommending the recommended commodity subclass and the recommended commodities to the user.
In some embodiments, the obtaining user history data further comprises:
and acquiring the historical consumption record of the user.
In some embodiments, the recommending merchandise based on the user history data and regional merchandise sales data further comprises:
acquiring a first commodity set according to the commodity browsing records;
acquiring a second commodity set according to the regional commodity sales data;
acquiring a commodity intersection of the first commodity set and the second commodity set;
sorting the commodities in the commodity intersection based on the browsed duration and the sales quantity of the commodities, and selecting a preset quantity of recommended commodity subclasses from high to low based on the sorting;
obtaining a price interval based on the historical consumption record;
and sorting the commodities in the recommended commodity subclass based on the browsed duration and the sales quantity of the commodities, selecting a preset quantity of recommended commodities with commodity prices in the price interval from high to low based on the sorting, and recommending the recommended commodity subclass and the recommended commodities to the user.
In a second aspect, an embodiment of the present application provides a commodity recommendation device, including:
the information acquisition module is used for acquiring user information;
the data acquisition module is used for acquiring user historical data based on the user information, and the user historical data comprises commodity browsing records;
the sales condition acquisition module is used for acquiring regional commodity sales data;
and the recommending module is used for recommending commodities based on the user history data and the regional commodity sales data.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the article recommendation method according to the first aspect is implemented.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the item recommendation method according to the first aspect.
Compared with the related art, the commodity recommendation method, the commodity recommendation device, the computer equipment and the storage medium provided by the embodiment of the application acquire the user information; acquiring user history data based on the user information, wherein the user history data comprises commodity browsing records; acquiring regional commodity sales data; the commodity recommending method based on the user historical data and the regional commodity sales data has the advantages that commodities are recommended based on the regional commodity sales data and the user commodity browsing records, the recommending condition is adjusted in real time according to the preference difference and the sales condition change of the user, personalized commodity recommending service can be provided for the user, and the recommending effect is good.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
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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 flowchart illustrating a commodity recommendation method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a merchandise recommendation method according to another embodiment of the present invention;
FIG. 3 is a block diagram of a merchandise recommendation device according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
Referring to fig. 1, fig. 1 is a flowchart illustrating a commodity recommendation method according to an embodiment of the invention.
In this embodiment, the commodity recommendation method includes:
s101, obtaining user information.
It can be understood that the user identity information is obtained to identify the user identity, and personalized recommendation is performed based on different users.
S102, obtaining user history data based on the user information, wherein the user history data comprises commodity browsing records.
For example, the user preference may be determined based on the user history data, so as to perform personalized recommendation on the user according to the user preference.
S103, obtaining regional commodity sales data.
Illustratively, the regional commodity sales data may be sales of commodities in a limited region, or may be overall sales of commodities, and the sales include information such as commodity types and sales numbers in a specific time.
And S104, recommending commodities based on the user history data and the regional commodity sales data.
Illustratively, user preferences are obtained based on user historical data, commodities with high sales popularity are obtained based on regional commodity sales data, personalized recommendation is carried out on the user by combining the two conditions, the browsing interest of the user can be improved, and the user viscosity is improved.
According to the commodity recommendation method, the user information is acquired; acquiring user history data based on the user information, wherein the user history data comprises commodity browsing records; acquiring regional commodity sales data; the commodity recommending method based on the regional commodity sales data has the advantages that the commodity recommending mode is carried out based on the user historical data and the regional commodity sales data, the commodity is recommended based on the regional commodity sales data and the commodity browsing records of the user, the recommending condition is adjusted in real time according to the preference difference and the sales condition change of the user, personalized commodity recommending service can be provided for the user, and the recommending effect is good.
In another embodiment, obtaining user information includes obtaining a user identity and a user equipment identity. It can be understood that the user identity is login information of the user, for example, information for identifying the user identity, such as a login account, a user ID, and the like; the user equipment identification is the unique identification of the equipment on which the user surfs the internet, the user equipment identification is bound with the user equipment, and even if the user does not log in an account on the app, the equipment can record the commodity browsing record of the user on the app.
In another embodiment, obtaining user history data based on the user information further comprises determining a user status based on the user identity; if the user state is the first state, acquiring user historical data based on the user equipment identification; and if the user state is the second state, acquiring user historical data based on the user identity. Illustratively, when the user state is the first state, it indicates that the user is a new guest, and when the user state is the second state, it indicates that the user is not a new guest. It can be understood that, whether the user logs in the shopping website or app according to the embodiment of the present invention for the first time is determined based on the user identity, if the user logs in the shopping website or app for the first time, the user is a new guest, and if the user does not log in the shopping website or app for the first time, the user is not a new guest. For example, if the user is a new customer, the user does not log in the shopping website or app, and no commodity browsing record exists in the corresponding account on the shopping website or app, so that the commodity browsing record on the shopping website or app is obtained based on the user device identifier when the user does not log in the account. And if the user is not a new customer, acquiring a commodity browsing record of the user on the shopping website or the app based on the user identity, namely the login information of the user.
In another embodiment, obtaining user history data based on the user information comprises: and acquiring a commodity browsing record of the user corresponding to the user information in a preset time period, and taking the commodity browsing record as user history data. Illustratively, the preset time period may be one month. It can be understood that the past month record of browsing the goods can reflect the recent preference of the user. In other embodiments, the time range of the obtained commodity browsing record may be adjusted according to actual requirements.
Illustratively, data collection of the user commodity browsing records is realized through app buried point data collection. Specifically, user data is collected through the app buried point, and the collected data is sent to the hdfs system (Hadoop distributed file system). Specifically, user behavior data is collected, and the collection time period is one month. Recording the operation of the user on the APP through the embedded point SDK, acquiring data such as user IP, equipment ID, user click, residence time and the like, preliminarily judging a commodity concerned by the user, and obtaining a corresponding ERP (Enterprise Resource Planning) subclass according to the commodity. Illustratively, only the commodity browsing records with the commodity staying time of 10s or more are screened, and the commodity browsing records are sorted according to the accumulation of the subclasses of staying time.
Enterprise Resource planning, ERP (Enterprise Resource planning), was introduced by Gartner Group, USA in 1990. Enterprise resource planning is the MRP II (Enterprise manufacturing resource planning) next generation manufacturing system and resource planning software. In addition to the functions of production resource planning, manufacturing, finance, sales, procurement, etc. already existing in MRP II, the system also has quality management, laboratory management, business process management, product data management, inventory, distribution and transportation management, human resource management and periodic reporting systems. At present, the meaning represented by ERP in China is expanded, and various types of software used for enterprises are generally brought into the ERP category. The system breaks the traditional enterprise boundary, optimizes the resources of the enterprise from the supply chain range, and is a new generation information system based on the network economic era. The method is mainly used for improving the business process of the enterprise so as to improve the core competitiveness of the enterprise.
ERP is a supply chain management concept proposed by the U.S. computer technical consulting and assessment Group Gartner Group Inc. The enterprise resource plan is a management platform which is established on the basis of information technology and provides decision operation means for an enterprise decision layer and employees by using a systematic management idea. The ERP system supports mixed manufacturing environments of discrete type, flow type and the like, the application range is expanded from the manufacturing industry to the public service departments of retail industry, service industry, banking industry, telecommunication industry, government and schools and the like, and enterprise resources are effectively integrated by fusing database technology, a graphical user interface, a fourth generation query language, a client server structure, a computer-aided development tool, a portable open system and the like.
Illustratively, the commodity sales condition is data collected from the ERP, which represents the data of the actual sales condition of a region, and the ordering is performed according to the actual amount of the singular number in the system, and represents popular sales subclasses and commodities of a certain region.
In another embodiment, the collected user data may further include a purchase price interval, a last consumption time, a last 30 days accumulated consumption amount, a last 60 days accumulated consumption amount, a last 90 days accumulated consumption amount, a purchased big category, a purchased sub category, a viewed commodity for more than 10 seconds, a viewed commodity price reading, a viewed C-end secondary category, a viewed C-end tertiary category, a recommended C-end tertiary category, a commodity ordering recommendation, and the like; the commodity data includes: browsing volume, visitor number, per-person browsing volume, jump rate, average dwell time, new visitor number, old visitor number, new user retention rate, number of additional customers, additional purchase conversion rate, number of exit visitors, exit rate, purchase conversion rate, and the like. It can be understood that different types of user data and commodity data can be collected according to actual application scenarios and requirements.
In another embodiment, obtaining regional merchandise sales data comprises: and acquiring the commodity sales condition of the current city of the user. It can be understood that the commodity sales condition of the city where the user is currently located can show the popularity of the commodity to a certain extent, and a certain reference function exists for the consideration of the preference of the user. In other embodiments, the region range of the obtained commodity sales condition may be adjusted according to actual needs.
In another embodiment, the step of obtaining the regional commodity sales data further comprises the step of obtaining the commodity sales condition of the city where the last consumption record of the user is located if the user is not a new customer. For example, the city where the last consumption record of the user is located may be a frequent residence place of the user to some extent, and the sales condition of the commodity in the place has a certain reference function on the consideration of the preference of the user.
In another embodiment, the commodity recommendation method further comprises training a data model, after the data model is trained, the subclasses and the commodities can be ranked according to the commodity browsing duration, the subclass browsing duration, the commodity sales volume and the subclass sales total input of the user, and the ranking of the subclasses and the commodity sales heat in the regional commodity sales data is obtained through the data model. Specifically, the sales data collected from ERP may be weighted higher than the data collected from APP. Regional commodity sales data are collected to a hdfs system (Hadoop distributed file system), and sales condition data collected from the ERP are mainly related to an order form from the ERP, so that subclasses of network points and sales popularity ranking of commodities are discharged.
In another embodiment, the making of the merchandise recommendation based on the user history data and the regional merchandise sales data comprises: acquiring a first commodity set according to the commodity browsing records; acquiring a second commodity set according to the regional commodity sales data; acquiring a commodity intersection of the first commodity set and the second commodity set; sorting the commodities in the commodity intersection based on the browsed duration and the sales quantity of the commodities, and selecting a preset quantity of recommended commodity subclasses from high to low based on the sorting; and sorting the commodities in the recommended commodity subclass based on the browsed duration and the sales quantity of the commodities, selecting a preset quantity of recommended commodities from high to low based on the sorting, and recommending the recommended commodity subclass and the recommended commodities to the user. Illustratively, if the user is a new guest, acquiring a commodity browsing record of the past month based on the user equipment identification; if the user has no commodity browsing record in the past one month, directly acquiring the commodity sales condition of the current city of the user, selecting a subclass 5 before the sales popularity ranking in the commodity sales ranking of the city and the website where the user is located as a recommendation subclass, and recommending 10 commodities before the sales popularity in the 5 recommendation subclasss to the user; if the user has a commodity browsing record in the past month, taking an intersection of the commodity set in the commodity browsing record in the past month of the user and the commodity set of the current city, taking 5 commodity subclasses with the top sales popularity ranking in the intersection as recommendation subclasses, and recommending 10 commodities with the top sales popularity in the 5 recommendation subclasses to the user. Illustratively, if the intersection of the set of the commodities in the commodity browsing records of the past month of the user and the set of the commodities in the current city is less than 5 commodity subclasses, filling the current city with the subclasses with the top sales ranks in the commodities in the current city until 5 commodity subclasses are complemented for recommendation. In other embodiments, the number of the selected recommended sub-categories and the number of the recommended commodities may be determined according to actual situations. It can be understood that the subclasses of commodities and the sales degree of the commodities can be ranked through the trained data model based on the browsed duration and the sales quantity of the commodities.
Illustratively, if the user is not a new customer, acquiring a commodity browsing record of the past month based on the user identity; if the user has no commodity browsing record in the past one month, directly acquiring the commodity sales condition of the city where the last consumption record of the user is located or the current city, selecting the commodity sales ranking of the city where the last consumption record is located, the website or the current city and website, and recommending the 10 commodities with the top sales popularity in the 5 types of recommendation subclasses to the user; if the user has a commodity browsing record in the past month, taking an intersection of the commodity set in the commodity browsing record in the past month of the user and the commodity set of the city where the last consumption record is located or the current city, taking 5 commodity subclasses with the top5 sales popularity ranking in the intersection as recommendation subclasses, and recommending 10 commodities with the top 10 sales popularity in the 5 recommendation subclasses to the user. Illustratively, if the intersection of the set of commodities in the commodity browsing records of the user in the past month and the set of commodities in the city where the last consumption record is located or the current city is less than 5 commodity subclasses, filling the commodities with the subclasses with the highest sales ranking in the city where the last consumption record is located or the current city until 5 commodity subclasses are complemented for recommendation. In other embodiments, the number of the selected recommended sub-categories and the number of the recommended commodities may be determined according to actual situations.
In another embodiment, obtaining the user history data further comprises: and acquiring the historical consumption record of the user.
In another embodiment, recommending commodities based on user history data and regional commodity sales data further comprises acquiring a first commodity set according to a commodity browsing record; acquiring a second commodity set according to the regional commodity sales data; acquiring a commodity intersection of the first commodity set and the second commodity set; sorting the commodities in the commodity intersection based on the browsed duration and the sales quantity of the commodities, and selecting a preset quantity of recommended commodity subclasses from high to low based on the sorting; obtaining a price interval based on the historical consumption record; sorting the commodities in the recommended commodity subclasses based on the browsed duration and the sales quantity of the commodities, selecting a preset quantity of recommended commodities with commodity prices in a price interval based on the sorting from high to low, recommending the recommended commodity subclasses and the recommended commodities to a user exemplarily, and after 5 recommended subclasses are obtained if the user is not a new customer, screening the commodities recommended by each subclass according to a screening interval of + 80% of the highest price and-20% of the lowest price in the historical consumption record of the user to obtain commodities in each subclass of which the prices are in line with the screening interval, and recommending 10 commodities with the highest sales heat to the user. In other embodiments, the price interval may be determined based on actual circumstances. In other embodiments, the number of the selected recommended sub-categories and the number of the recommended commodities may be determined according to actual situations.
In another embodiment, if the goods obtained by screening are out of stock, other goods are selected for filling according to business requirements. For example, a product labeled "new product", "money for explosion" may be selected for filling. In other embodiments, other types of commodities can be selected for filling, and the filling can be determined according to actual needs.
Referring to fig. 2, fig. 2 is a flowchart illustrating a product recommendation method according to another embodiment of the invention.
Exemplarily, after a user logs in an APP or a website, user information is obtained, whether a new customer is available is judged based on the user information, if the user is the new customer, a website sales ranking is obtained according to a city where a latest IP is located and a website, browsing data in the latest month is obtained, intersection data is obtained based on the website sales ranking and commodity browsing records in the latest month, commodity subclasses of a sales ranking TOP5 are selected, if the intersection is less than 5 commodity subclasses, subclasses which are ranked earlier are selected from the sales ranking and filled until 5 recommended commodity subclasses are complemented, recommended commodities which are 10 th highest in sales popularity ranking in each recommended commodity subclass are obtained, and the recommended commodity subclasses and the recommended commodities are recommended to the user; if the user is a regular customer, obtaining a sales ranking corresponding to a city and a website according to the last consumption record, obtaining browsing data of the latest month and historical consumption records of the customer, obtaining intersection data based on the sales ranking of the website and the browsing records of commodities of the latest month, selecting commodity subclasses of a sales ranking TOP5, if less than 5 commodity subclasses exist in the intersection, selecting subclasses with the TOP ranking from the sales ranking to fill until 5 recommended commodity subclasses are complemented, defining a price interval according to the historical consumption records of the customer, selecting recommended commodities which accord with the defined price interval in each recommended commodity subclass and have the sales heat ranking of 10, and recommending the recommended commodity subclasses and the recommended commodities to the user.
According to the commodity recommendation method, the user information is acquired; acquiring user history data based on the user information, wherein the user history data comprises commodity browsing records; acquiring regional commodity sales data; the commodity recommending method based on the regional commodity sales data has the advantages that the commodity recommending mode is carried out based on the user historical data and the regional commodity sales data, the commodity is recommended based on the regional commodity sales data and the commodity browsing records of the user, the recommending condition is adjusted in real time according to the preference difference and the sales condition change of the user, personalized commodity recommending service can be provided for the user, and the recommending effect is good.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The embodiment further provides a commodity recommending device, which is used for implementing the above embodiments and preferred embodiments, and the description of the device is omitted. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 3 is a block diagram of a structure of an article recommendation device according to an embodiment of the present application, and as shown in fig. 3, the device includes:
and the information acquisition module 10 is used for acquiring the user information.
The information obtaining module 10 is further configured to obtain a user identity and a user equipment identity.
And the data acquisition module 20 is configured to acquire user history data based on the user information, where the user history data includes a commodity browsing record.
The data acquisition module 20 is further configured to:
determining a user state based on the user identity;
if the user state is the first state, acquiring user historical data based on the user equipment identification;
and if the user state is the second state, acquiring user historical data based on the user identity.
The data obtaining module 20 is further configured to obtain a commodity browsing record of the user corresponding to the user information in a preset time period, and use the commodity browsing record as user history data.
The data obtaining module 20 is further configured to obtain a historical consumption record of the user if the user is not a new guest.
And the sales condition acquisition module 30 is used for acquiring regional commodity sales data.
And the recommending module 40 is used for recommending commodities based on the user history data and the regional commodity sales data.
The recommending module 40 is further configured to:
acquiring a first commodity set according to the commodity browsing records;
acquiring a second commodity set according to the regional commodity sales data;
acquiring a commodity intersection of the first commodity set and the second commodity set;
sorting the commodities in the commodity intersection based on the browsed duration and the sales quantity of the commodities, and selecting a preset quantity of recommended commodity subclasses from high to low based on the sorting;
and sorting the commodities in the recommended commodity subclass based on the browsed duration and the sales quantity of the commodities, selecting a preset quantity of recommended commodities from high to low based on the sorting, and recommending the recommended commodity subclass and the recommended commodities to the user.
The recommending module 40 is further configured to:
acquiring a first commodity set according to the commodity browsing records;
acquiring a second commodity set according to the regional commodity sales data;
acquiring a commodity intersection of the first commodity set and the second commodity set;
sorting the commodities in the commodity intersection based on the browsed duration and the sales quantity of the commodities, and selecting a preset quantity of recommended commodity subclasses from high to low based on the sorting;
obtaining a price interval based on the historical consumption record;
and sorting the commodities in the recommended commodity subclass based on the browsed duration and the sales quantity of the commodities, and recommending the recommended commodity subclass and the recommended commodities to the user based on the preset quantity of recommended commodities with the commodity price in the price interval, wherein the commodity price is selected from high to low in the sorting.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
In addition, the commodity recommendation method described in conjunction with fig. 1 in the embodiment of the present application may be implemented by a computer device. Fig. 4 is a hardware structure diagram of a computer device according to an embodiment of the present application.
The computer device may include a processor 41 and a memory 42 storing computer program instructions.
Specifically, the processor 41 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The processor 41 reads and executes the computer program instructions stored in the memory 42 to implement any one of the article recommendation methods in the above embodiments.
In some of these embodiments, the computer device may also include a communication interface 43 and a bus 40. As shown in fig. 4, the processor 41, the memory 42, and the communication interface 43 are connected via the bus 40 to complete mutual communication.
The communication interface 43 is used for implementing communication between modules, devices, units and/or apparatuses in the embodiments of the present application. The communication interface 43 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The computer device may execute the commodity recommendation method in the embodiment of the present application based on the acquired computer program instruction, thereby implementing the commodity recommendation method described in conjunction with fig. 1.
In addition, in combination with the commodity recommendation method in the foregoing embodiment, the embodiment of the present application may be implemented by providing a computer-readable storage medium. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the article recommendation methods in the above embodiments.
According to the commodity recommendation method, the commodity recommendation device, the computer equipment and the storage medium, the user information is obtained; acquiring user history data based on the user information, wherein the user history data comprises commodity browsing records; acquiring regional commodity sales data; the commodity recommending method based on the regional commodity sales data has the advantages that the commodity recommending mode is carried out based on the user historical data and the regional commodity sales data, the commodity is recommended based on the regional commodity sales data and the commodity browsing records of the user, the recommending condition is adjusted in real time according to the preference difference and the sales condition change of the user, personalized commodity recommending service can be provided for the user, and the recommending effect is good.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for recommending an article, comprising:
acquiring user information;
acquiring user history data based on the user information, wherein the user history data comprises commodity browsing records;
acquiring regional commodity sales data;
and recommending commodities based on the user history data and the regional commodity sales data.
2. The commodity recommendation method according to claim 1, wherein said acquiring user information includes:
and acquiring a user identity identifier and a user equipment identifier.
3. The item recommendation method according to claim 2, wherein said obtaining user history data based on the user information further comprises:
determining a user status based on the user identity;
if the user state is a first state, acquiring the user historical data based on the user equipment identification;
and if the user state is a second state, acquiring the user historical data based on the user identity.
4. The item recommendation method according to claim 1, wherein said obtaining user history data based on the user information comprises:
and acquiring a commodity browsing record of the user corresponding to the user information in a preset time period, and taking the commodity browsing record as user history data.
5. The product recommendation method according to claim 4, wherein the performing product recommendation based on the user history data and regional product sales data includes:
acquiring a first commodity set according to the commodity browsing records;
acquiring a second commodity set according to the regional commodity sales data;
acquiring a commodity intersection of the first commodity set and the second commodity set;
sorting the commodities in the commodity intersection based on the browsed duration and the sales quantity of the commodities, and selecting a preset quantity of recommended commodity subclasses from high to low based on the sorting;
and sorting the commodities in the recommended commodity subclass based on the browsed duration and the sales quantity of the commodities, selecting a preset quantity of recommended commodities from high to low based on the sorting, and recommending the recommended commodity subclass and the recommended commodities to the user.
6. The item recommendation method according to claim 4, wherein said obtaining user history data further comprises:
and acquiring the historical consumption record of the user.
7. The product recommendation method according to claim 6, wherein the performing product recommendation based on the user history data and regional product sales data further comprises:
acquiring a first commodity set according to the commodity browsing records;
acquiring a second commodity set according to the regional commodity sales data;
acquiring a commodity intersection of the first commodity set and the second commodity set;
sorting the commodities in the commodity intersection based on the browsed duration and the sales quantity of the commodities, and selecting a preset quantity of recommended commodity subclasses from high to low based on the sorting;
obtaining a price interval based on the historical consumption record;
and sorting the commodities in the recommended commodity subclass based on the browsed duration and the sales quantity of the commodities, selecting a preset quantity of recommended commodities with commodity prices in the price interval from high to low based on the sorting, and recommending the recommended commodity subclass and the recommended commodities to the user.
8. An article recommendation device, comprising:
the information acquisition module is used for acquiring user information;
the data acquisition module is used for acquiring user historical data based on the user information, and the user historical data comprises commodity browsing records;
the sales condition acquisition module is used for acquiring regional commodity sales data;
and the recommending module is used for recommending commodities based on the user history data and the regional commodity sales data.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the item recommendation method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium on which a computer program is stored, the program, when being executed by a processor, implementing an article recommendation method according to any one of claims 1 to 7.
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