Method, electronic device and storage medium for product recommendation

Abstract

The present application relates to the field of information retrieval technology, and discloses a method for product recommendation, including: responding to a product browsing request initiated by a user at the first time; determining a second product to be recommended from the products in the product database other than the first product to be recommended; the first product to be recommended is a number of products determined in response to the product browsing request initiated by the user before the first time; storing the second product to be recommended in a local server; determining a target product in the local server according to the user's behavior data information; recommending the target product to the user; and deleting the target product in the local server. In this way, when recommending products to the user, the products stored in the local server are all products that the user has never browsed, and thus products that have not been browsed can be recommended to the user. The present application also discloses an electronic device and a storage medium.

Description

Technical Field

[0001] The present application relates to the field of information retrieval technology, for example, to a method, electronic device and storage medium for commodity recommendation. Background Art

[0002] In order to ensure that users are interested in the recommended products, e-shopping platforms usually make personalized product recommendations for users based on their behavioral data, thereby increasing their desire to buy.

[0003] In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in the related art:

[0004] Since the existing method of recommending products usually calculates the corresponding score of each product based on the user's behavior data, and then pushes the product with the highest score to the user, this often recommends products that the user has already browsed to the user again, resulting in a poor user experience. Summary of the invention

[0005] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0006] The embodiments of the present disclosure provide a method, an electronic device, and a storage medium for product recommendation, so as to recommend products that have not been browsed to users.

[0007] In some embodiments, the method for product recommendation includes: responding to a product browsing request initiated by a user at a first time; determining a second product to be recommended from products in a product database other than the first product to be recommended; the first product to be recommended is a number of products determined in response to the product browsing request initiated by the user before the first time; storing the second product to be recommended in a local server; determining a target product in the local server based on the user's behavior data information; recommending the target product to the user; and deleting the target product in the local server.

[0008] In some embodiments, the electronic device includes a processor and a memory storing program instructions, and the processor is configured to execute the above-mentioned method for product recommendation when running the program instructions.

[0009] In some embodiments, the storage medium stores program instructions, and when the program instructions are run, the above-mentioned method for product recommendation is executed.

[0010] The method, electronic device and storage medium for product recommendation provided by the embodiments of the present disclosure can achieve the following technical effects: by responding to the product browsing request initiated by the user at the first time; determining the second product to be recommended from the products in the product database other than the first product to be recommended; and storing the second product to be recommended in the local server; then determining a preset number of target products in the local server according to the user's behavior data information; and recommending the target products to the user; and deleting the target products in the local server at the same time. In this way, the query can be optimized, so that when recommending products to the user, the products stored in the local server are all products that the user has not browsed, and thus the products that have not been browsed can be recommended to the user.

[0011] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0013] Figure 1 is a schematic diagram of a method for product recommendation provided by an embodiment of the present disclosure;

[0014] Figure 2 is a schematic diagram of another method for product recommendation provided by an embodiment of the present disclosure;

[0015] Figure 3 is a schematic diagram of another method for product recommendation provided by an embodiment of the present disclosure;

[0016] Figure 4 is a schematic diagram of another method for product recommendation provided by an embodiment of the present disclosure;

[0017] Figure 5 is a schematic diagram of another method for product recommendation provided by an embodiment of the present disclosure;

[0018] Figure 6 It is a schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0020] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0021] Unless otherwise stated, the term "plurality" means two or more.

[0022] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0023] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0024] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0025] The present application is applied to product recommendation in an online shopping mall, by determining a second product to be recommended from products in a product database other than a first product to be recommended, and determining a target product in a local server according to user behavior data, and then recommending the target product to the user, and deleting the target product from the local server; in this way, it can be ensured that when recommending products to a user, the products stored in the local server are all products that the user has never browsed; and thus products that have not been browsed and meet the user's preferences can be recommended to the user.

[0026] Combination Figure 1 As shown, the present disclosure provides a method for product recommendation, including:

[0027] Step S101, the electronic device responds to a product browsing request initiated by a user at a first time; determines a second product to be recommended from products in a product database other than the first product to be recommended; the first product to be recommended is a number of products determined in response to the product browsing request initiated by the user at a first time;

[0028] Step S102, the electronic device stores the second recommended product in the local server;

[0029] Step S103, the electronic device determines the target product in the local server according to the user's behavior data information;

[0030] Step S104: the electronic device recommends the target product to the user; and deletes the target product in the local server.

[0031] The method for product recommendation provided by the embodiment of the present disclosure is adopted, by determining the second product to be recommended from the products in the product database except the first product to be recommended and storing it in the local server, and then determining the target product in the local server; and deleting the target product from the local server after recommending the target product to the user. This ensures that when recommending products to the user, the products stored in the local server are products that the user has never browsed, and thus it is possible to recommend products that the user has never browsed.

[0032] Optionally, determining the second product to be recommended from the products in the product database other than the first product to be recommended includes: obtaining the earliest storage time from the storage times corresponding to the first products to be recommended; determining the time of the product browsing request initiated by the user before the first time as the second time; determining the products with storage times between the earliest storage time and the second time as the first candidate products in the product database; and determining the second product to be recommended from the first candidate products. In this way, by determining the products with storage times between the earliest storage time and the second time as the first candidate products, the cardinality of the first candidate products can be reduced, so that the second product to be recommended can be determined more quickly from the first candidate products.

[0033] Combination Figure 2 As shown, the present disclosure provides a method for product recommendation, including:

[0034] Step S201, the electronic device responds to the product browsing request initiated by the user at the first time, and obtains the earliest entry time from the entry times corresponding to the first recommended products; the first recommended products are several products determined in response to the product browsing request initiated by the user before the first time.

[0035] Step S202, the electronic device determines the time of the product browsing request initiated by the user before the first time as the second time;

[0036] Step S203, the electronic device determines, in the commodity database, commodities whose storage time is between the earliest storage time and the second time as first candidate commodities;

[0037] Step S204, the electronic device determines a second commodity to be recommended from the first candidate commodities;

[0038] Step S205, the electronic device stores the second recommended product in the local server;

[0039] Step S206, the electronic device determines the target product in the local server according to the user's behavior data information;

[0040] Step S207: the electronic device recommends the target product to the user; and deletes the target product in the local server.

[0041] The method for recommending products provided by the embodiment of the present disclosure is adopted, by determining the products whose storage time is between the earliest storage time and the second time as the first candidate products, and determining the second recommended product from the first candidate products and storing it in the local server, and then determining the target product in the local server; then recommending the target product to the user, and deleting the target product from the local server. In this way, when recommending products to the user, the cardinality of the first candidate products can be reduced, so that the products that the user has not browsed can be determined more quickly in the product database, thereby ensuring that the products stored in the local server are products that the user has not browsed, and then the products that have not browsed can be recommended to the user.

[0042] Optionally, the earliest warehousing time is obtained from the warehousing times corresponding to the first commodities to be recommended, including: sorting the first commodities to be recommended in descending order according to the warehousing time; and determining the warehousing time of the first commodity to be recommended that ranks first as the second time.

[0043] In some embodiments, when a user initiates multiple product browsing requests before a first time, the second time corresponding to each product browsing request is obtained; in response to each product browsing request, a number of first products to be recommended corresponding to each second time is determined; the number of first products to be recommended corresponding to each second time are sorted in descending order according to the warehousing time; and the warehousing time of the first product to be recommended that ranks first is determined as the earliest warehousing time.

[0044] Optionally, the earliest entry time is obtained from the entry times corresponding to each first commodity to be recommended in the following manner: in response to a commodity browsing request initiated by the user before the first time, the first commodities to be recommended are determined in the commodity database in order of entry time from the latest to the earliest; the entry time of the last determined first commodity to be recommended is determined as the earliest entry time.

[0045] Optionally, determining the second product to be recommended from the first product candidates includes: obtaining topic keywords from the user's search record; selecting a first preset number of first product candidates associated with the topic keywords from the first product candidates in order of entry time from the last to the first; and determining the selected first product candidates as the second product to be recommended.

[0046] Optionally, the behavior data information includes the user's browsed historical commodity information and the user's purchased historical commodity information, and the second candidate commodity in the local server includes the first commodity to be recommended and the second commodity to be recommended; determining a preset number of target commodities in the local server according to the user's behavior data information includes: obtaining a number of first scores corresponding to each second candidate commodity according to the historical commodity information; summing up a number of first scores corresponding to each second candidate commodity to obtain a second score corresponding to each second candidate commodity; sorting each second candidate commodity in descending order according to the second score; and determining the first second preset number of second candidate commodities as target commodities. In this way, it can be ensured that the commodities recommended to the user are commodities that meet the user's preferences, thereby increasing the user's desire to buy.

[0047] Combination Figure 3 As shown, the present disclosure provides a method for product recommendation, including:

[0048] Step S301, the electronic device responds to the product browsing request initiated by the user at the first time; determines a second product to be recommended from the products in the product database other than the first product to be recommended; the first product to be recommended is a number of products determined in response to the product browsing request initiated by the user before the first time;

[0049] Step S302, the electronic device stores the second recommended product in the local server;

[0050] Step S303, the electronic device obtains a number of first scores corresponding to each second candidate product according to the historical product information; the second candidate products include the first product to be recommended and the second product to be recommended;

[0051] Step S304, the electronic device sums up a number of first scores corresponding to each second candidate product to obtain a second score corresponding to each second candidate product;

[0052] Step S305, the electronic device sorts the second candidate products in descending order according to the second scores;

[0053] Step S306: The electronic device determines the first second preset number of second candidate commodities as target commodities.

[0054] Step S307: the electronic device recommends the target product to the user; and deletes the target product in the local server.

[0055] By adopting the method for product recommendation provided by the embodiment of the present disclosure, a second product to be recommended is determined from the products in the product database other than the first product to be recommended and stored in the local server, and then a target product is determined in the local server; and after the target product is recommended to the user, the target product is deleted from the local server, so that when recommending products to the user, the products stored in the local server are products that the user has never browsed. Therefore, products that have not been browsed can be recommended to the user.

[0056] In some embodiments, the user's search record is "Beijing Winter Games", where the topic keyword in the search record is "Winter Games", and M first candidate products related to "Winter Games" are selected from the first candidate products in descending order of inventory time; for example, ice hockey, ice Dun Dun, etc.; the selected M first candidate products associated with the topic keyword are all determined as second products to be recommended.

[0057] Optionally, the historical product information includes the product name and the historical time corresponding to the product name; obtaining a number of first scores corresponding to each second candidate product based on the historical product information, including: obtaining the association between the second candidate product and each product name; obtaining a weight coefficient corresponding to each product name based on the historical time; and determining the product of each association score and each weight coefficient as a number of first scores corresponding to the second candidate product.

[0058] Combination Figure 4 As shown, the present disclosure provides a method for product recommendation, including:

[0059] Step S401, the electronic device responds to the product browsing request initiated by the user at the first time; determines a second product to be recommended from the products in the product database other than the first product to be recommended; the first product to be recommended is a number of products determined in response to the product browsing request initiated by the user before the first time;

[0060] Step S402, the electronic device stores the second recommended product in the local server;

[0061] Step S403, the electronic device obtains the relevance between the second candidate product and each product name; the second candidate product includes the first product to be recommended and the second product to be recommended;

[0062] Step S404, the electronic device obtains the weight coefficient corresponding to each product name according to the historical time;

[0063] Step S405, the electronic device determines the product of each associated score and each weight coefficient as a plurality of first scores corresponding to the second candidate product;

[0064] Step S406, the electronic device sums up the first scores corresponding to the second candidate products to obtain the second scores corresponding to the second candidate products;

[0065] Step S407, the electronic device sorts the second candidate products in descending order according to the second scores;

[0066] Step S408: The electronic device determines the first second preset number of second candidate commodities as target commodities.

[0067] Step S409: the electronic device recommends the target product to the user; and deletes the target product in the local server.

[0068] By adopting the method for product recommendation provided by the embodiment of the present disclosure, a second product to be recommended is determined from the products in the product database other than the first product to be recommended and stored in the local server, and then a target product is determined in the local server; and after the target product is recommended to the user, the target product is deleted from the local server, so that when recommending products to the user, the products stored in the local server are products that the user has never browsed. Therefore, products that have not been browsed can be recommended to the user.

[0069] Optionally, the later the historical time corresponding to the product name is, the smaller the weight coefficient corresponding to the product name is.

[0070] Optionally, obtaining the weight coefficients corresponding to the product names according to the historical time includes: obtaining the weight coefficients corresponding to the historical time in a preset data table, wherein the data table stores the correspondence between the historical time and the weight coefficient. In this way, products that meet the user's recent preferences can be recommended to the user.

[0071] Optionally, the behavior data information includes user-selected preference keywords; the second candidate products in the local server include the first product to be recommended and the second product to be recommended; determining the target product in the local server based on the user's behavior data information includes: obtaining the correlation between each second candidate product and the preference keywords respectively; sorting each second candidate product in descending order according to the correlation; and determining the first second preset number of second candidate products as the target product.

[0072] Combination Figure 5 As shown, the present disclosure provides a method for product recommendation, including:

[0073] Step S501, the electronic device responds to the product browsing request initiated by the user at the first time; determines a second product to be recommended from the products in the product database other than the first product to be recommended; the first product to be recommended is a number of products determined in response to the product browsing request initiated by the user before the first time;

[0074] Step S502, the electronic device stores the second recommended product in the local server;

[0075] Step S503, the electronic device obtains the relevance between each second candidate product and the preferred keyword; the second candidate products include the first to-be-recommended product and the second to-be-recommended product;

[0076] Step S504, the electronic device sorts the second candidate products in descending order according to the relevance;

[0077] Step S505: The electronic device determines the first second preset number of second candidate commodities as target commodities.

[0078] Step S506: the electronic device recommends the target product to the user; and deletes the target product in the local server.

[0079] By adopting the method for product recommendation provided by the embodiment of the present disclosure, a second product to be recommended is determined from the products in the product database other than the first product to be recommended and stored in the local server, and then a target product is determined in the local server; and after the target product is recommended to the user, the target product is deleted from the local server, so that when recommending products to the user, the products stored in the local server are products that the user has never browsed. Therefore, products that have not been browsed can be recommended to the user.

[0080] Optionally, the quantity of the target products is less than or equal to the quantity of the second candidate products.

[0081] Optionally, the commodities in the commodity database are stored in order of storage time from earliest to latest.

[0082] In some embodiments, in response to a product browsing request initiated by a user at time1, since the user has not initiated a product browsing request before time1, all products in the product database are determined as the first alternative products; the topic keywords of the user's search records are obtained, and m second products to be recommended associated with the topic keywords are determined from the first alternative products in the order from the latest to the earliest storage time; and they are stored in the local database; n target products are determined in the local server according to the user's behavior data information; where n ≤ m; the target products are recommended to the user, and the target products in the local server are deleted; at this time, the remaining quantity of products in the local server is m - n. Then, in response to a product browsing request initiated by the user at time2, where time1 < time2; the m second products to be recommended are sorted in descending order according to the storage time; the storage time of the second product to be recommended ranked first is determined as the second time timeA, and products with storage times outside the range from timeA to time1 are determined as the first alternative products in the product database; and y second products to be recommended are determined therefrom, and then the determined y second products to be recommended are stored in the local server, and at this time, the number of products stored in the local server is m - n + y; then x target products are determined in the local server according to the user's behavior data information; and the x target products are recommended to the user. In this way, as long as the products in the product database are not all retrieved, products that the user has not browsed can be recommended to the user.

[0083] As shown in Figure 6 FIG. 5, an embodiment of the present disclosure provides an electronic device, including a processor 600 and a memory 601. Optionally, the device may further include a communication interface 602 and a bus 603. Among them, the processor 600, the communication interface 602, and the memory 601 can communicate with each other through the bus 603. The communication interface 602 can be used for information transmission. The processor 600 can call the logical instructions in the memory 601 to execute the method for product recommendation in the above embodiment.

[0084] By using the electronic device provided in the embodiment of the present disclosure, by determining the second products to be recommended from the products in the product database other than the first products to be recommended and storing them in the local server, and then determining the target products in the local server; and deleting the target products from the local server after recommending the target products to the user, it can be ensured that the products stored in the local server are products that the user has not browsed when recommending products to the user. Furthermore, products that the user has not browsed can be recommended to the user.

[0085] Optionally, the electronic device is a mobile phone, a computer, or a tablet computer.

[0086] In addition, the logic instructions in the memory 601 described above can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0087] The memory 601 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 600 executes the function application and data processing by running the program instructions / modules stored in the memory 601, that is, the method for product recommendation in the above embodiment is implemented.

[0088] The memory 601 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 601 may include a high-speed random access memory and may also include a non-volatile memory.

[0089] An embodiment of the present disclosure provides a storage medium storing program instructions, which, when run, execute the above-mentioned method for product recommendation.

[0090] An embodiment of the present disclosure provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the above-mentioned method for product recommendation.

[0091] The computer-readable storage medium mentioned above may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0092] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0093] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0094] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0095] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0096] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for product recommendation, It is characterized in that include: Respond to the product browsing request initiated by the user at the first time; Determine a second commodity to be recommended from commodities in the commodity database other than the first commodity to be recommended; the first commodity to be recommended is a number of commodities determined in response to a commodity browsing request initiated by the user at a first time; Storing the second recommended product in the local server; Determine the target product in the local server based on the user's behavior data information; Recommend target products to users; And delete the target product in the local server; Determining the second commodity to be recommended from commodities in the commodity database other than the first commodity to be recommended, including: obtaining the earliest entry time from the entry times corresponding to the first commodities to be recommended; The time of the product browsing request initiated by the user before the first time is determined as the second time; in the product database, the products whose storage time is between the earliest storage time and the second time are determined as the first candidate products; and the second product to be recommended is determined from the first candidate products.

2. The method according to claim 1, It is characterized in that The earliest entry time is obtained from the entry times corresponding to the first recommended products, including: Sort the first recommended products in descending order according to the time of entry into the warehouse; The warehousing time of the first recommended product ranked first is determined as the earliest warehousing time.

3. The method according to claim 1, It is characterized in that Determining the second recommended product from the first candidate products includes: Get the topic keywords of the user's search history; Selecting a first preset number of first candidate products associated with the topic keyword from the first candidate products in the order of the storage time from the latest to the earliest; The selected first candidate product is determined as the second product to be recommended.

4. The method according to claim 1, It is characterized in that The behavior data information includes historical product information browsed by the user and historical product information purchased by the user, and the second candidate product in the local server includes the first product to be recommended and the second product to be recommended; Determine the target product in the local server based on the user's behavior data information, including: Obtaining a number of first scores corresponding to each second candidate product according to historical product information; Summing a plurality of first scores corresponding to each second candidate product to obtain a second score corresponding to each second candidate product; Sort the second candidate products in descending order according to the second scores; The first second preset number of second candidate commodities are determined as target commodities.

5. The method according to claim 4, It is characterized in that The historical product information includes the product name and the historical time corresponding to the product name; obtaining a plurality of first scores corresponding to each second candidate product according to the historical product information includes: Obtaining the relevance between the second candidate product and each product name; Obtain the weight coefficient corresponding to each product name according to historical time; The products of the associated scores and the weight coefficients are determined as a plurality of first scores corresponding to the second candidate products.

6. The method according to claim 5, It is characterized in that Obtain the weight coefficient corresponding to each product name according to historical time, including: The weight coefficient corresponding to the historical time is obtained in a preset data table, and the corresponding relationship between the historical time and the weight coefficient is stored in the data table.

7. The method according to claim 1, It is characterized in that The behavior data information includes the favorite keywords selected by the user; the second candidate products in the local server include the first product to be recommended and the second product to be recommended; Determine the target product in the local server based on the user's behavior data information, including: Obtaining the relevance between each second candidate product and the preferred keyword respectively; Sort the second candidate products in descending order according to their relevance; The first second preset number of second candidate commodities are determined as target commodities.

8. An electronic device comprising a processor and a memory storing program instructions, It is characterized in that The processor is configured to execute the method for product recommendation according to any one of claims 1 to 7 when running the program instructions.

9. A storage medium storing program instructions, It is characterized in that When the program instructions are executed, the method for product recommendation according to any one of claims 1 to 7 is executed.

Images