Method and device for determining to-be-recommended item, storage medium and electronic equipment

Abstract

The application discloses a method and device for determining a recommended item, a storage medium and an electronic device. The method comprises: obtaining historical behavior data of a target object in a first period, classifying the historical behavior data, and dividing the historical behavior data into explicit data or implicit data; obtaining a first score corresponding to the explicit data and a second score corresponding to the implicit data; determining a first weight value corresponding to the explicit data and a second weight value corresponding to the implicit data; and determining whether to recommend a predetermined type of item to the target object in a second period based on the first score, the second score, the first weight value and the second weight value, wherein the end time of the first period is earlier than the start time of the second period. The application solves the technical problems of inaccurate recommendation results, waste of recommendation resources and poor user experience caused by the related art of recommending items to users based on the explicit behaviors of users, such as scoring and evaluating items.

Description

Technical Field

[0001] This application relates to the field of big data recommendation, and more specifically, to a method, apparatus, storage medium, and electronic device for determining items to be recommended. Background Technology

[0002] In the field of content recommendation based on big data analytics, related technologies typically determine whether to recommend a particular type of item to a user based on explicit behaviors such as user ratings and reviews of that type of item. For users who have a habit of rating and reviewing, recommending items based on this method is relatively accurate. However, some users do not have the habit of rating or reviewing items after purchasing them. Therefore, recommending items based on user ratings can lead to inaccurate recommendations, lower user purchase intentions, and technical problems that affect user experience.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method, apparatus, storage medium, and electronic device for determining items to be recommended, in order to at least solve the technical problems of inaccurate recommendation results, wasted recommendation resources, and poor user experience caused by related technologies recommending items to users based on explicit behaviors such as user ratings and scores of items.

[0005] According to one aspect of the embodiments of this application, a method for determining items to be recommended is provided, comprising: acquiring historical behavior data of a target object in a first time period, wherein the historical behavior data includes at least: purchase behavior data; classifying the historical behavior data into explicit data or implicit data, wherein the explicit data includes at least: the target object's evaluation of a predetermined type of item, and the implicit data includes at least: the number of clicks by the target object on the predetermined type of item; acquiring a first rating corresponding to the explicit data and a second rating corresponding to the implicit data; determining a first weight value corresponding to the explicit data and a second weight value corresponding to the implicit data; and determining whether to recommend the predetermined type of item to the target object in a second time period based on the first rating, the second rating, the first weight value, and the second weight value, wherein the end time of the first time period is earlier than the start time of the second time period.

[0006] Optionally, where the first weight value and the second weight value are both default weight values, determining whether to recommend a predetermined type of item to the target object in the second time period based on the first score, the second score, the first weight value, and the second weight value includes: adjusting the first weight value and the second weight value according to historical behavior data to obtain a third weight value and a fourth weight value; obtaining a first product of the first score and the third weight value, and a second product of the second score and the fourth weight value; determining a first sum of the first product and the second product, and recommending a predetermined type of item to the target object in the second time period if the first sum is greater than a preset threshold.

[0007] Optionally, the first weight value and the second weight value are adjusted according to historical behavioral data to obtain the third weight value and the fourth weight value, including: determining the number of items purchased by the target object in the first time period and the number of times the purchased items have been evaluated based on historical behavioral data; determining the target ratio of the number of times the purchased items have been evaluated to the number of purchased items; and adjusting the first weight value and the second weight value based on the target ratio to obtain the third weight value and the fourth weight value.

[0008] Optionally, the first weight value and the second weight value are adjusted based on the target ratio to obtain the third weight value and the fourth weight value, including: obtaining a preset ratio, determining the difference between the target ratio and the preset ratio; determining the absolute value of the difference and the sign of the difference; adjusting the first weight value and the second weight value according to the absolute value of the difference and the sign of the difference to obtain the third weight value and the fourth weight value.

[0009] Optionally, the first weight value and the second weight value are adjusted according to the absolute value of the difference and the sign of the difference to obtain the third weight value and the fourth weight value, including: when the sign is positive, determining the sum of the first weight value and the absolute value as the third weight value, and determining the difference between the second weight value and the absolute value as the fourth weight value.

[0010] Optionally, the first weight value and the second weight value are adjusted according to the absolute value of the difference and the sign of the difference to obtain the third weight value and the fourth weight value, including: when the sign is negative, determining the difference between the first weight value and the absolute value as the third weight value, and determining the sum of the second weight value and the absolute value as the fourth weight value.

[0011] Optionally, determining whether to recommend a predetermined type of item to the target object in the second time period based on the first score, the second score, the first weight value, and the second weight value, wherein the end time of the first time period is earlier than the start time of the second time period, includes: obtaining a third product of the first score and the first weight value, and a fourth product of the second score and the second weight value; determining a second sum of the third product and the fourth product, and recommending a predetermined type of item to the target object in the second time period if the second sum is greater than a preset threshold.

[0012] Optionally, obtaining historical behavior data of the target object in the first time period includes: determining the terminal identifier corresponding to the target object, and calling a data crawling algorithm to crawl the historical behavior data corresponding to the terminal identifier from the database.

[0013] According to another aspect of the embodiments of this application, an apparatus for determining items to be recommended is also provided, comprising: a first acquisition module, configured to acquire historical behavior data of a target object in a first time period, wherein the historical behavior data includes at least: purchase behavior data; a classification module, configured to classify the historical behavior data into explicit data or implicit data, wherein the explicit data includes at least: the target object's evaluation of a predetermined type of item, and the implicit data includes at least: the number of clicks by the target object on the predetermined type of item; a second acquisition module, configured to acquire a first rating corresponding to the explicit data and a second rating corresponding to the implicit data; a first determination module, configured to determine a first weight value corresponding to the explicit data and a second weight value corresponding to the implicit data; and a second determination module, configured to determine whether to recommend a predetermined type of item to the target object in a second time period based on the first rating, the second rating, the first weight value, and the second weight value, wherein the end time of the first time period is earlier than the start time of the second time period.

[0014] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any method for determining items to be recommended.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement any method for determining items to be recommended.

[0016] In this embodiment, a weighted analysis of users' explicit and implicit behaviors is employed. By acquiring users' historical behavior data in the first time period and classifying this data into explicit and implicit data, the system determines whether to recommend a pre-defined type of item to the user in the second time period based on the first rating and first weight value corresponding to the explicit data, and the second rating and second weight value corresponding to the implicit data. This improves the accuracy of item recommendations, minimizes invalid recommendations, and indirectly increases the order transaction success rate. Furthermore, it solves the technical problems of inaccurate recommendations, wasted recommendation resources, and poor user experience caused by related technologies that recommend items based on users' explicit behaviors such as item ratings. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 This is a flowchart illustrating an optional method for determining items to be recommended according to an embodiment of this application;

[0019] Figure 2 This is a schematic diagram of an optional device for determining items to be recommended according to an embodiment of this application. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] According to an embodiment of this application, an embodiment of a method for determining items to be recommended is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0023] Figure 1 This is a method for determining items to be recommended according to embodiments of this application, such as... Figure 1 As shown, the method includes the following steps:

[0024] Step S102: Obtain the historical behavior data of the target object in the first time period, wherein the historical behavior data includes at least: purchase behavior data;

[0025] Step S104: Classify the historical behavior data into explicit data or implicit data. Explicit data includes at least the target object's evaluation of a predetermined type of item, and implicit data includes at least the target object's click count for a predetermined type of item.

[0026] Step S106: Obtain the first score corresponding to the explicit data and the second score corresponding to the implicit data;

[0027] It should be noted that the explicit data mentioned above includes user reviews, ratings, and comments. However, this also presents certain problems. For example, users rarely participate in comments, resulting in inaccurate explicit rating data or data that only provides partial information; once a user rates a product, the rating is not updated. The implicit data mentioned above mainly refers to user click behavior, purchase behavior, and search behavior, which implicitly reveal users' preferences for products.

[0028] Step S108: Determine the first weight value corresponding to the explicit data and the second weight value corresponding to the implicit data;

[0029] Step S110: Based on the first score, the second score, the first weight value, and the second weight value, determine whether to recommend a predetermined type of item to the target object in the second time period, wherein the end time of the first time period is earlier than the start time of the second time period.

[0030] This method for determining items to be recommended employs a weighted analysis of users' explicit and implicit behaviors. It acquires users' historical behavior data from the first time period and categorizes this data into explicit and implicit data. Then, based on the first rating and first weight value corresponding to the explicit data, and the second rating and second weight value corresponding to the implicit data, it determines whether to recommend a pre-defined type of item to the user in the second time period. This improves the accuracy of item recommendations, minimizes invalid recommendations, and indirectly increases the order transaction success rate. Furthermore, it solves the technical problems caused by related technologies that recommend items based on users' explicit behaviors such as item ratings, resulting in inaccurate recommendations, wasted recommendation resources, and poor user experience.

[0031] The above classification of explicit and implicit data can be performed using Bayes' theorem, such as the Naive Bayesian Classifier. The principle behind this classification is explained below:

[0032] If a given dataset contains M categories, the Naive Bayes classification method can predict whether a given observation belongs to the specific category with the highest posterior probability. In other words, the Naive Bayes classification method predicts that X belongs to category C if and only if:

[0033] ;

[0034] If we maximize at this point ,That Largest class This is known as the maximum a posteriori hypothesis, according to Bayes' theorem:

[0035] ;

[0036] It can be seen that since P(X) is equal for all categories, it is only necessary to... Take the largest value.

[0037] To predict the class of an unknown sample X, one can perform a multiplication table for each class. Estimate the corresponding .

[0038] .

[0039] It should be noted that the first weight value and the second weight value are both default weight values. In some embodiments of this application, determining whether to recommend a predetermined type of item to the target object in the second time period based on the first score, the second score, the first weight value, and the second weight value can be achieved through the following steps: Specifically, the first weight value and the second weight value can be adjusted according to historical behavior data to obtain the third weight value and the fourth weight value; the first product of the first score and the third weight value, and the second product of the second score and the fourth weight value are obtained; the first sum of the first product and the second product is determined; if the first sum is greater than a preset threshold, the predetermined type of item is recommended to the target object in the second time period.

[0040] As an optional implementation, the first weight value and the second weight value are adjusted according to historical behavior data to obtain the third weight value and the fourth weight value, including: determining the number of items purchased by the target object in the first time period and the number of times the purchased items have been evaluated based on historical behavior data; determining the target ratio of the number of times the purchased items have been evaluated to the number of purchased items; and adjusting the first weight value and the second weight value based on the target ratio to obtain the third weight value and the fourth weight value.

[0041] Specifically, the first and second weight values ​​are adjusted based on the target ratio to obtain the third and fourth weight values. This can be done by obtaining a preset ratio, determining the difference between the target ratio and the preset ratio, determining the absolute value and sign of the difference, and adjusting the first and second weight values ​​based on the absolute value and sign of the difference to obtain the third and fourth weight values.

[0042] In some embodiments of this application, the first weight value and the second weight value are adjusted based on the absolute value and the sign of the difference to obtain the third weight value and the fourth weight value. This includes: when the sign is positive, determining the sum of the first weight value and its absolute value as the third weight value, and determining the difference between the second weight value and its absolute value as the fourth weight value. It is understood that when the sign is negative, determining the difference between the first weight value and its absolute value as the third weight value, and determining the sum of the second weight value and its absolute value as the fourth weight value.

[0043] For example, if the first and second weight values ​​are both 50%, the preset ratio is 50%, and the target ratio is 60%, then the absolute value of the difference is 10%, and the sign of the difference is positive. Therefore, the third weight value is 10% + 50% = 60%, and the fourth weight value is 50% - 10% = 40%. It's understandable that by dynamically adjusting the third weight value corresponding to explicit data and the fourth weight value corresponding to implicit data, the user's purchase intention can be more accurately reflected, resulting in a more accurate recommendation result.

[0044] In some embodiments of this application, determining whether to recommend a predetermined type of item to a target object in a second time period is based on a first score, a second score, a first weight value, and a second weight value, wherein the end time of the first time period is earlier than the start time of the second time period, includes: obtaining a third product of the first score and the first weight value, and a fourth product of the second score and the second weight value; determining a second sum of the third product and the fourth product, and recommending a predetermined type of item to the target object in the second time period if the second sum is greater than a preset threshold.

[0045] In some embodiments of this application, obtaining historical behavior data of a target object in a first time period can be achieved in the following way: the terminal identifier corresponding to the target object can be determined, and a data crawling algorithm can be called to crawl the historical behavior data corresponding to the terminal identifier from the database.

[0046] It's easy to notice that while some users rate the items they've purchased, others don't. However, the items a user has viewed and clicked on cannot be hidden. Therefore, it's possible to determine which items to recommend to a user based solely on implicit data. Specifically, this can be achieved by identifying the number of clicks on two types of items together.

[0047] For example, user click behavior data for the day can be obtained, and some noisy data, such as missing product information, can be filtered out. This allows the acquisition of information such as the user's session ID, product ID (product identifier), and browsing time.

[0048] For example, the browsing time for A4 differs significantly from that for A1, A2, and A3, so it is filtered out; here, it is defined as 1800 seconds.

[0049] Calculate the number of clicks that a person makes on two products together, and then calculate the product similarity based on the number of clicks.

[0050] ;

[0051] Where s(i,j) represents the similarity between items i and j; The frequencies of occurrence of both i and j are expressed as follows: freq(i) represents the frequency of occurrence of i; freq(j) represents the frequency of occurrence of j.

[0052] Then, the product similarity data from the previous day can be merged, and the product classification probability of product similarity can be comprehensively judged. Products with higher similarity can be selected as new product similarity, thereby realizing the calculation of incremental product similarity, and then determining whether to recommend items to users. For example, if products A1 and A2 have high similarity, both products A1 and A2 can be recommended to users at the same time.

[0053] Figure 2 This is an apparatus for determining items to be recommended according to embodiments of this application, such as... Figure 2 As shown, the device includes:

[0054] The first acquisition module 20 is used to acquire the historical behavior data of the target object in the first time period, wherein the historical behavior data includes at least: purchase behavior data;

[0055] Classification module 22 is used to classify historical behavior data, dividing it into explicit data or implicit data;

[0056] The second acquisition module 24 is used to acquire the first rating corresponding to explicit data and the second rating corresponding to implicit data; wherein, the explicit data includes at least: the target object's evaluation of the predetermined type of item, and the implicit data includes at least: the number of clicks by the target object on the predetermined type of item;

[0057] The first determining module 26 is used to determine the first weight value corresponding to the explicit data and the second weight value corresponding to the implicit data;

[0058] The second determining module 28 is used to determine whether to recommend a predetermined type of item to the target object in the second time period based on the first score, the second score, the first weight value, and the second weight value, wherein the end time of the first time period is earlier than the start time of the second time period.

[0059] In this device for determining items to be recommended, a first acquisition module 20 is used to acquire historical behavior data of the target object in a first time period, wherein the historical behavior data includes at least: purchase behavior data; a classification module 22 is used to classify the historical behavior data, dividing it into explicit data or implicit data; a second acquisition module 24 is used to acquire a first rating corresponding to the explicit data and a second rating corresponding to the implicit data; a first determination module 26 is used to determine a first weight value corresponding to the explicit data and a second weight value corresponding to the implicit data; and a second determination module 28 is used to determine whether to recommend a predetermined type of item to the target object in a second time period based on the first rating, the second rating, the first weight value, and the second weight value, wherein the end time of the first time period is earlier than the start time of the second time period, thereby improving the accuracy of item recommendations, minimizing invalid recommendations, and indirectly improving the order transaction success rate. This solves the technical problems caused by related technologies recommending items to users based on explicit behaviors such as user ratings and evaluations, resulting in inaccurate recommendation results, wasted recommendation resources, and poor user experience.

[0060] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any method for determining items to be recommended.

[0061] Specifically, the aforementioned storage medium is used to store program instructions for the following functions, thereby implementing the following functions:

[0062] Acquire historical behavioral data of the target audience in the first time period, wherein the historical behavioral data includes at least: purchase behavior data; classify the historical behavioral data into explicit data or implicit data, wherein the explicit data includes at least: the target audience's evaluation of a predetermined type of item, and the implicit data includes at least: the number of clicks by the target audience on the predetermined type of item; acquire the first rating corresponding to the explicit data and the second rating corresponding to the implicit data; determine the first weight value corresponding to the explicit data and the second weight value corresponding to the implicit data; based on the first rating, the second rating, the first weight value, and the second weight value, determine whether to recommend the predetermined type of item to the target audience in the second time period, wherein the end time of the first time period is earlier than the start time of the second time period.

[0063] Optionally, in this embodiment, the storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of the storage medium include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0064] In an exemplary embodiment of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the method for determining items to be recommended as described above.

[0065] Optionally, when executed by a processor, the computer program may perform the following steps:

[0066] Acquire historical behavioral data of the target audience in the first time period, wherein the historical behavioral data includes at least: purchase behavior data; classify the historical behavioral data into explicit data or implicit data, wherein the explicit data includes at least: the target audience's evaluation of a predetermined type of item, and the implicit data includes at least: the number of clicks by the target audience on the predetermined type of item; acquire the first rating corresponding to the explicit data and the second rating corresponding to the implicit data; determine the first weight value corresponding to the explicit data and the second weight value corresponding to the implicit data; based on the first rating, the second rating, the first weight value, and the second weight value, determine whether to recommend the predetermined type of item to the target audience in the second time period, wherein the end time of the first time period is earlier than the start time of the second time period.

[0067] An electronic device is provided according to an embodiment of the present application, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the methods described above for determining an article to be determined.

[0068] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0069] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0070] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0071] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0072] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0073] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0074] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0075] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0076] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0078] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0080] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0081] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining items to be recommended, characterized in that, include: Obtain historical behavioral data of the target object in a first time period, wherein the historical behavioral data includes at least: purchase behavior data; The historical behavior data is classified into explicit data and implicit data. The explicit data includes at least the target object's evaluation of a predetermined type of item, and the implicit data includes at least the number of clicks made by the target object on the predetermined type of item. Obtain the first score corresponding to the explicit data and the second score corresponding to the implicit data; Determine the first weight value corresponding to the explicit data and the second weight value corresponding to the implicit data; Based on the first score, the second score, the first weight value, and the second weight value, it is determined whether to recommend the predetermined type of item to the target object in the second time period, wherein the end time of the first time period is earlier than the start time of the second time period; The method of determining whether to recommend the predetermined type of item to the target object in the second time period based on the first rating, the second rating, the first weight value, and the second weight value includes: determining the number of items purchased by the target object in the first time period and the number of times the purchased items have been evaluated based on the historical behavior data; determining a target ratio of the number of times the purchased items have been evaluated to the number of purchased items; obtaining a preset ratio and determining the difference between the target ratio and the preset ratio; determining the absolute value of the difference and the sign of the difference; if the sign is positive, determining the sum of the first weight value and the absolute value as a third weight value, and determining the difference between the second weight value and the absolute value as a fourth weight value; if the sign is negative, determining the difference between the first weight value and the absolute value as the third weight value, and determining the sum of the second weight value and the absolute value as the fourth weight value; obtaining a first product of the first rating and the third weight value, and a second product of the second rating and the fourth weight value; determining a first sum of the first product and the second product, and recommending the predetermined type of item to the target object in the second time period if the first sum is greater than a preset threshold.

2. The method according to claim 1, characterized in that, Both the first weight value and the second weight value are default weight values.

3. The method according to claim 1, characterized in that, Based on the first score, the second score, the first weight value, and the second weight value, it is determined whether to recommend the predetermined type of item to the target object in the second time period, wherein the end time of the first time period is earlier than the start time of the second time period, including: Obtain the third product of the first score and the first weight value, and the fourth product of the second score and the second weight value; Determine the second sum of the third product and the fourth product, and if the second sum is greater than a preset threshold, recommend the predetermined type of item to the target object during the second time period.

4. The method according to any one of claims 1 to 3, characterized in that, Obtain historical behavior data of the target object in the first time period, including: Determine the terminal identifier corresponding to the target object, and call the data crawling algorithm to crawl the historical behavior data corresponding to the terminal identifier from the database.

5. An apparatus for determining an item to be recommended, characterized in that, include: The first acquisition module is used to acquire historical behavior data of the target object in a first time period, wherein the historical behavior data includes at least: purchase behavior data; A classification module is used to classify the historical behavior data, dividing the historical behavior data into explicit data and implicit data. The explicit data includes at least the target object's evaluation of a predetermined type of item, and the implicit data includes at least the number of clicks made by the target object on the predetermined type of item. The second acquisition module is used to acquire the first score corresponding to the explicit data and the second score corresponding to the implicit data; The first determining module is used to determine the first weight value corresponding to the explicit data and the second weight value corresponding to the implicit data; The second determining module is used to determine whether to recommend the predetermined type of item to the target object in the second time period based on the first score, the second score, the first weight value and the second weight value, wherein the end time of the first time period is earlier than the start time of the second time period. The method of determining whether to recommend the predetermined type of item to the target object in the second time period based on the first rating, the second rating, the first weight value, and the second weight value includes: determining the number of items purchased by the target object in the first time period and the number of times the purchased items have been evaluated based on the historical behavior data; determining a target ratio of the number of times the purchased items have been evaluated to the number of purchased items; obtaining a preset ratio and determining the difference between the target ratio and the preset ratio; determining the absolute value of the difference and the sign of the difference; if the sign is positive, determining the sum of the first weight value and the absolute value as a third weight value, and determining the difference between the second weight value and the absolute value as a fourth weight value; if the sign is negative, determining the difference between the first weight value and the absolute value as the third weight value, and determining the sum of the second weight value and the absolute value as the fourth weight value; obtaining a first product of the first rating and the third weight value, and a second product of the second rating and the fourth weight value; determining a first sum of the first product and the second product, and recommending the predetermined type of item to the target object in the second time period if the first sum is greater than a preset threshold.

6. A non-volatile storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the method for determining items to be recommended as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method for determining items to be recommended as described in any one of claims 1 to 4.

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