CN115907906A - Method and device for determining to-be-recommended article, storage medium and electronic equipment - Google Patents
Method and device for determining to-be-recommended article, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses a method and device for determining an article to be recommended, a storage medium and electronic equipment. Wherein, the method comprises the following steps: acquiring 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; acquiring a first score corresponding to the dominant data and a second score corresponding to the recessive data; determining a first weight value corresponding to the dominant data and a second weight value corresponding to the recessive data; and determining whether to recommend the predetermined type of item to the target object for a second period of time based on the first score, the second score, the first weight value and the second weight value, wherein the ending time of the first period of time is earlier than the starting time of the second period of time. The method and the device solve the technical problems that in the related art, the recommendation result is inaccurate, recommendation resources are wasted and user experience is poor due to the fact that the related technology recommends the articles to the user based on explicit behaviors such as evaluation and scoring of the articles by the user.
Description
Technical Field
The application relates to the field of big data recommendation, in particular to a method and device for determining an article to be recommended, a storage medium and electronic equipment.
Background
In the field of content recommendation based on big data analysis, in the related art, whether to recommend a type of article to a user is generally determined based on explicit behaviors of rating and grading of the user on the type of article, for the user with a rating and grading habit, recommending the article based on the above method is relatively accurate, but some users do not have the habit of rating or grading on the purchased article, so that the technical problems that recommending the article to the user based on the rating of the user is inaccurate, the purchase intention of the user is low, and the user experience is influenced are caused.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a method, a device, a storage medium and electronic equipment for determining an article to be recommended, so as to at least solve the technical problems that recommendation results are inaccurate, recommendation resources are wasted and user experience is poor due to the fact that articles are recommended to users based on explicit behaviors of users such as evaluation scoring of the articles.
According to an aspect of an embodiment of the present application, there is provided a method for determining an item to be recommended, including: acquiring historical behavior data of a target object in a first period, wherein the historical behavior data at least comprises the following steps: purchasing behavior data; classifying historical behavior data, and dividing the historical behavior data into explicit data or implicit data, wherein the explicit data at least comprises the following steps: the target object evaluates the predetermined type of article, and the implicit data at least comprises: the number of clicks of the target object on the predetermined type of article; acquiring a first score corresponding to the dominant data and a second score corresponding to the recessive data; determining a first weight value corresponding to the dominant data and a second weight value corresponding to the recessive data; and determining whether to recommend the predetermined type of item to the target object for a second period of time based on the first score, the second score, the first weight value and the second weight value, wherein the ending time of the first period of time is earlier than the starting time of the second period of time.
Optionally, wherein the first weight value and the second weight value are default weight values, and determining whether to recommend a predetermined type of item to the target object at the second time period based on the first score, the second score, the first weight value, and the second weight value includes: respectively 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; acquiring 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; and determining a first sum of the first product and the second product, and recommending the target object with the predetermined type of item in the second period of time if the first sum is larger than a preset threshold value.
Optionally, the adjusting the first weight value and the second weight value according to the historical behavior data to obtain a third weight value and a fourth weight value respectively includes: determining the number of purchased articles of the target object in a first period and the number of times of evaluating the purchased articles according to the historical behavior data; determining a target ratio of the number of evaluations of the purchased item to the number of purchased items; and adjusting the first weight value and the second weight value based on the target ratio to obtain a third weight value and a fourth weight value.
Optionally, adjusting the first weight value and the second weight value based on the target ratio to obtain a third weight value and a fourth weight value, including: acquiring a preset ratio, and determining a difference value between a target ratio and the preset ratio; determining the absolute value of the difference value and the sign of the difference value; and adjusting the first weight value and the second weight value according to the absolute value of the difference value and the sign of the difference value to obtain a third weight value and a fourth weight value.
Optionally, 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 a third weight value and a fourth weight value, including: and under the condition that the sign is positive, determining that the sum of the first weight value and the absolute value is a third weight value, and determining that the difference value of the second weight value and the absolute value is a fourth weight value.
Optionally, the 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 a third weight value and a fourth weight value includes: and under the condition that the sign is negative, determining that the difference value between the first weight value and the absolute value is a third weight value, and determining that the sum value of the second weight value and the absolute value is a fourth weight value.
Optionally, determining 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 an ending time of the first time period is earlier than a starting time of the second time period includes: obtaining a third product of the first weight value and the second weight value and a fourth product of the second weight value and the second weight value; and determining a second sum of the third product and the fourth product, and recommending the target object with the predetermined type of item in the second period of time if the second sum is larger than a preset threshold value.
Optionally, acquiring historical behavior data of the target object in the first period of time includes: and determining a terminal identifier corresponding to the target object, and calling a data crawling algorithm to crawl historical behavior data corresponding to the terminal identifier from a database.
According to another aspect of the embodiments of the present application, there is also provided an apparatus for determining an item to be recommended, including: the first obtaining module is used for obtaining historical behavior data of the target object in a first time period, wherein the historical behavior data at least comprises: purchasing behavior data; the classification module is used for classifying the historical behavior data and dividing the historical behavior data into explicit data or implicit data, wherein the explicit data at least comprises the following components: the target object evaluates the predetermined type of article, and the implicit data at least comprises: the number of clicks of the target object on the predetermined type of article; the second acquisition module is used for acquiring a first score corresponding to the dominant data and a second score corresponding to the recessive data; the first determining module is used for determining a first weight value corresponding to the dominant data and a second weight value corresponding to the recessive data; and the second determination module is used for determining whether to recommend the predetermined type of item to the target object in a second time period based on the first score, the second score, the first weight value and the second weight value, wherein the ending time of the first time period is earlier than the starting time of the second time period.
According to another aspect of the embodiments of the present application, a non-volatile storage medium is further provided, where the storage medium includes a stored program, and when the program runs, a device in which the storage medium is located is controlled to execute any one of the methods for determining an item to be recommended.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement any of the methods of determining an item to be recommended.
In the embodiment of the application, a mode of performing weighted analysis on explicit and implicit behaviors of a user is adopted, historical behavior data of the user in a first time period is obtained, the historical behavior data is classified into explicit data and implicit data, then whether a preset type of articles are recommended to the user in a second time period is determined based on a first score and a first weight value corresponding to the explicit data and a second score and a second weight value corresponding to the implicit data, so that the accuracy of article recommendation is improved, invalid recommendation is avoided as much as possible, the technical effect of the order trading success rate is indirectly improved, and the technical problems that the recommendation result is inaccurate, recommendation resources are wasted, and user experience is poor due to the fact that the articles are recommended to the user based on the explicit behaviors of the user such as rating and scoring the like of the articles in the related technology are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart diagram illustrating an alternative method for determining an item to be recommended according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an alternative apparatus for determining an item to be recommended according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present application, there is provided an embodiment of a method for determining an item to be recommended, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a method for determining an item to be recommended according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
step S102, obtaining historical behavior data of the target object in a first time period, wherein the historical behavior data at least comprises: purchasing behavior data;
step S104, classifying the historical behavior data, and dividing the historical behavior data into explicit data or implicit data, wherein the explicit data at least comprises the following steps: the target object evaluates the preset type of articles, and the implicit data at least comprises the following components: the number of clicks of the target object on the predetermined type of article;
step S106, acquiring a first score corresponding to the dominant data and a second score corresponding to the recessive data;
note that, the above explicit data: including user comment, scoring, comment and other data. However, certain problems also exist, such as that the user participates in the comment rarely, so that the explicit scoring data is inaccurate or only partial information is given; once the user scores, the user score value and the like are not updated. The implicit data is as follows: mainly refers to user clicking behavior, purchasing behavior, searching behavior and the like, and the preference of the user to commodities is implicitly disclosed by the data.
Step S108, determining a first weight value corresponding to the dominant data and a second weight value corresponding to the recessive data;
step S110, it is determined whether to recommend the predetermined type of item to the target object in a second time period based on the first score, the second score, the first weight value, and the second weight value, wherein an end time of the first time period is earlier than a start time of the second time period.
According to the method for determining the to-be-recommended articles, a mode of carrying out weighted analysis on explicit and implicit behaviors of a user is adopted, historical behavior data of the user in a first time period is obtained, the historical behavior data are classified into explicit data and implicit data, then whether predetermined types of articles are recommended to the user in a second time period is determined based on a first score and a first weight value corresponding to the explicit data and a second score and a second weight value corresponding to the implicit data, and therefore the technical effects of improving article recommendation accuracy, avoiding invalid recommendation as much as possible, further indirectly improving order trading success rate are achieved, and the technical problems that recommendation results are inaccurate, recommendation resources are wasted, and user experience is poor due to the fact that related technologies recommend the articles to the user based on explicit behaviors such as rating of the user on the articles are solved.
The above classification of dominant data and recessive data can be classified by Bayesian theorem, for example, nave Bayesian Classifier (Naive Bayesian Classifier), and the following principle is introduced:
if there are M classification classes in a given dataset, it can be predicted, by a naive bayes classification method, whether a given observation belongs to a particular class with the highest a posteriori probability, that is, the naive bayes classification method predicts that X belongs to class C, meaning if and only if:
P(C i |X)＞P(C j |X)1≤j≤m,j≠i；
at this time, if P (C) is maximized i I X), P (C) thereof i | X) largest class C i Referred to as maximum a posteriori hypothesis, according to bayes' theorem:
it can be seen that since P (X) is equal for all classes, only P (X | C) is required i )P(C i ) The maximum value is obtained.
To predict the class of an unknown sample X, one may predict for each class C i Estimate the corresponding P (X | C) i )P(C i )。
P(C i |X)＞P(C j |X)1≤j≤m,j≠i。
It should be noted that, the first weight value and the second weight value are both default weight values, and in some embodiments of the present application, whether to recommend a predetermined type of object to the target object at the second time period is determined based on the first score, the second score, the first weight value, and the second weight value, which may be implemented by the following steps, specifically, the first weight value and the second weight value may be respectively adjusted 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; and determining a first sum of the first product and the second product, and recommending the target object with the predetermined type of item in the second period of time if the first sum is larger than a preset threshold value.
As an optional implementation manner, the adjusting the first weight value and the second weight value according to the historical behavior data to obtain a third weight value and a fourth weight value respectively includes: determining the number of purchased articles of the target object in a first period and the number of times of evaluating the purchased articles according to the historical behavior data; determining a target ratio of the number of evaluations of the purchased item to the number of purchased items; and adjusting the first weight value and the second weight value based on the target ratio to obtain a third weight value and a fourth weight value.
Specifically, the first weight value and the second weight value are adjusted based on the target ratio to obtain a third weight value and a fourth weight value, and a difference value between the target ratio and the preset ratio can be determined for obtaining the preset ratio; determining the absolute value of the difference value and the sign of the difference value; and adjusting the first weight value and the second weight value according to the absolute value of the difference value and the sign of the difference value to obtain a third weight value and a fourth weight value.
In some embodiments of the present application, 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 a third weight value and a fourth weight value includes: and under the condition that the sign is positive, determining that the sum of the first weight value and the absolute value is a third weight value, and determining that the difference value of the second weight value and the absolute value is a fourth weight value. It is to be understood that, in the case where the sign is a negative sign, the difference between the first weight value and the absolute value is determined as a third weight value, and the sum of the second weight value and the absolute value is determined as a fourth weight value.
For example, the first weight value and the second weight value are both 50%, the preset ratio is 50%, the target ratio is 60%, the absolute value of the difference is 10%, the sign of the difference is positive, the third weight value is 10% +50% =60%, and the fourth weight value is 50% -10% =40%. It can be understood that the purchase intention of the user can be reflected more accurately by dynamically adjusting the third weight value corresponding to the dominant data and the fourth weight value corresponding to the recessive data, so that the final recommendation result is more accurate.
In some embodiments of the present application, determining whether to recommend the predetermined type of item to the target object for the second time period based on the first score, the second score, the first weight value, and the second weight value, wherein an end time of the first time period is earlier than a start time of the second time period includes: acquiring a third product of the first score and the second weight value and a fourth product of the second score and the second weight value; and determining a second sum of the third product and the fourth product, and recommending the target object with the predetermined type of item in the second period of time if the second sum is larger than a preset threshold value.
In some embodiments of the present application, obtaining historical behavior data of a target object in a first time period may be implemented as follows: and determining a terminal identifier corresponding to the target object, and calling a data crawling algorithm to crawl historical behavior data corresponding to the terminal identifier from a database.
It is easy to note that, since some users may evaluate the purchased articles, and some users may not have a habit of evaluating products, but the articles browsed and clicked by the users cannot be hidden, the articles recommended to the users may be determined based on implicit data alone, and specifically, the articles recommended to the users may be determined by determining the number of times of co-clicking between two types of articles.
For example, click behavior data of the user on the current day can be acquired, and some noise data, such as missing commodity information, can be filtered out. Thereby obtaining the information such as the sessionID, the commodity ID (commodity identification), the browsing time and the like of the user session.
For example: the browsing time of A4 is much different from A1, A2, A3, and is therefore filtered out, here defined as 1800 seconds,
and calculating the co-click times among the 2 commodities of the personnel, and calculating the commodity similarity according to the co-click times.
Wherein s (i, j) represents the similarity between the terms i and j; freq (i ≈ j) represents the frequency of co-occurrence of i and j: freq (i) represents the frequency of occurrence of i; freq (j) represents the frequency of occurrence of j.
And then, commodity similarity data of the previous day can be combined, commodity classification probability of commodity similarity is comprehensively judged, and a commodity with larger similarity is selected as new commodity similarity, so that commodity increment similarity calculation is realized, and whether an article is recommended to a user is further determined, for example, the commodity similarity of A1 and A2 is higher, and the article A1 and the article A2 can be recommended to the user at the same time.
Fig. 2 is a device for determining an item to be recommended according to an embodiment of the present application, and as shown in fig. 2, the device includes:
a first obtaining module 20, configured to obtain historical behavior data of the target object in a first time period, where the historical behavior data at least includes: purchasing behavior data;
the classification module 22 is configured to classify the historical behavior data, and divide the historical behavior data into explicit data or implicit data;
a second obtaining module 24, configured to obtain a first score corresponding to the explicit data and a second score corresponding to the implicit data; wherein the explicit data comprises at least: the target object evaluates the preset type of articles, and the implicit data at least comprises the following components: the number of clicks of the target object on the predetermined type of article;
a first determining module 26, configured to determine a first weight value corresponding to the dominant data and a second weight value corresponding to the recessive data;
and a second determining module 28, configured to determine whether to recommend the predetermined type of item to the target object in a second time period based on the first score, the second score, the first weight value, and the second weight value, wherein an ending time of the first time period is earlier than a starting time of the second time period.
In the device for determining an item to be recommended, a first obtaining module 20 is configured to obtain historical behavior data of a target object in a first time period, where the historical behavior data at least includes: purchasing behavior data; the classification module 22 is configured to classify the historical behavior data, and divide the historical behavior data into explicit data or implicit data; a second obtaining module 24, configured to obtain a first score corresponding to the explicit data and a second score corresponding to the implicit data; a first determining module 26, configured to determine a first weight value corresponding to the explicit data and a second weight value corresponding to the implicit data; the second determining module 28 is configured to determine whether to recommend a predetermined type of object to the target object at the second time period based on the first score, the second score, the first weight value, and the second weight value, where an ending time of the first time period is earlier than an initial time of the second time period, so that accuracy of recommending the object is improved, invalid recommendation is avoided as much as possible, and a technical effect of indirectly improving a success rate of order trading is achieved, and further technical problems that recommendation results are inaccurate, recommendation resources are wasted, and user experience is poor due to the fact that the related art recommends the object to the user based on explicit behaviors of the user such as rating and scoring of the object are solved.
According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, where the storage medium includes a stored program, and when the program runs, the apparatus on which the storage medium is located is controlled to execute any method for determining an item to be recommended.
Specifically, the storage medium is used for storing program instructions of the following functions, and the following functions are realized:
acquiring historical behavior data of a target object in a first period, wherein the historical behavior data at least comprises the following steps: purchasing behavior data; classifying historical behavior data, and dividing the historical behavior data into explicit data or implicit data, wherein the explicit data at least comprises the following steps: the target object evaluates the preset type of articles, and the implicit data at least comprises the following components: the number of clicks of the target object on the predetermined type of article; acquiring a first score corresponding to the dominant data and a second score corresponding to the recessive data; determining a first weight value corresponding to the dominant data and a second weight value corresponding to the recessive data; and determining whether to recommend the predetermined type of item to the target object for a second period of time based on the first score, the second score, the first weight value and the second weight value, wherein the ending time of the first period of time is earlier than the starting time of the second period of time.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the aforementioned storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the aforementioned.
In an exemplary embodiment of the present application, there is also provided a computer program product, including a computer program, which when executed by a processor, implements the method of determining an item to be recommended of any of the above.
Optionally, the computer program may, when executed by a processor, implement the steps of:
acquiring historical behavior data of a target object in a first period, wherein the historical behavior data at least comprises the following steps: purchasing behavior data; classifying historical behavior data, and dividing the historical behavior data into explicit data or implicit data, wherein the explicit data at least comprises the following steps: the target object evaluates the preset type of articles, and the implicit data at least comprises the following components: the number of clicks of the target object on the predetermined type of article; acquiring a first score corresponding to the dominant data and a second score corresponding to the recessive data; determining a first weight value corresponding to the dominant data and a second weight value corresponding to the recessive data; and determining whether to recommend the predetermined type of item to the target object for a second period of time based on the first score, the second score, the first weight value and the second weight value, wherein the ending time of the first period of time is earlier than the starting time of the second period of time.
An embodiment according to the present application provides an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any one of the above methods of determining an item to be determined.
Optionally, the electronic device may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a 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 may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
The above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the advantages and disadvantages of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (11)
1. A method of determining an item to be recommended, comprising:
acquiring historical behavior data of a target object in a first period, wherein the historical behavior data at least comprises the following steps: purchasing behavior data;
classifying the historical behavior data, and dividing the historical behavior data into explicit data or implicit data, wherein the explicit data at least comprises: the target object is used for evaluating a predetermined type of article, and the implicit data at least comprises the following data: the number of clicks of the target object on the preset type of article;
acquiring a first score corresponding to the dominant data and a second score corresponding to the recessive data;
determining a first weight value corresponding to the dominant data and a second weight value corresponding to the recessive data;
determining whether to recommend the predetermined type of item to the target object for a second period of time based on the first score, the second score, the first weight value, and the second weight value, wherein an end time of the first period of time is earlier than a start time of the second period of time.
2. The method of claim 1, wherein the first weight value and the second weight value are both default weight values, and wherein determining whether to recommend the predetermined type of item to the target object for a second period of time based on the first score, the second score, the first weight value, and the second weight value comprises:
respectively adjusting the first weight value and the second weight value according to the 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 the predetermined type of items to the target object in the second time period if the first sum is larger than a preset threshold value.
3. The method of claim 2, wherein adjusting the first weight value and the second weight value according to the historical behavior data to obtain a third weight value and a fourth weight value respectively comprises:
determining the quantity of purchased articles of the target object in the first period and the times of evaluating the purchased articles according to the historical behavior data;
determining a target ratio of a number of evaluations of the purchased item to a number of the purchased item;
and adjusting the first weight value and the second weight value based on the target ratio to obtain a third weight value and a fourth weight value.
4. The method of claim 3, wherein adjusting the first weight value and the second weight value based on the target ratio to obtain a third weight value and a fourth weight value comprises:
acquiring a preset ratio, and determining the difference value between the target ratio and the preset ratio;
determining the absolute value of the difference value and the sign of the difference value; and adjusting the first weight value and the second weight value according to the absolute value of the difference value and the sign of the difference value to obtain a third weight value and a fourth weight value.
5. The method of claim 4, wherein 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 a third weight value and a fourth weight value comprises:
and when the sign is a positive sign, determining that the sum of the first weight value and the absolute value is the third weight value, and determining that the difference between the second weight value and the absolute value is the fourth weight value.
6. The method of claim 4, wherein 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 a third weight value and a fourth weight value comprises:
in a case where the sign is a negative sign, determining that a difference value between the first weight value and the absolute value is the third weight value, and determining that a sum value of the second weight value and the absolute value is the fourth weight value.
7. The method of claim 1, wherein determining whether to recommend the predetermined type of item to the target object for a second period of time based on the first score, the second score, the first weight value, and the second weight value, wherein an end time of the first period of time is earlier than a start time of the second period of time comprises:
obtaining a third product of the first score and the second 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 the predetermined type of items to the target object in the second time period if the second sum is larger than a preset threshold value.
8. The method as claimed in any one of claims 1 to 7, wherein obtaining historical behavior data of the target object over the first time period comprises:
and determining a terminal identification corresponding to the target object, and calling a data crawling algorithm to crawl the historical behavior data corresponding to the terminal identification from a database.
9. An apparatus for determining an item to be recommended, comprising:
the device comprises a first obtaining module, a second obtaining module, a third obtaining module and a fourth obtaining module, wherein the first obtaining module is used for obtaining historical behavior data of a target object in a first time period, and the historical behavior data at least comprises: purchasing behavior data;
a classification module, configured to classify the historical behavior data, and divide the historical behavior data into explicit data or implicit data, where the explicit data at least includes: the target object evaluates the preset type of articles, and the implicit data at least comprises the following data: the number of clicks of the target object on the preset type of article;
the second acquisition module is used for acquiring a first score corresponding to the dominant data and a second score corresponding to the recessive data;
a first determining module, configured to determine a first weight value corresponding to the explicit data and a second weight value corresponding to the implicit data;
a second determination module, configured to determine whether to recommend the predetermined type of item to the target object in a second time period based on the first score, the second score, the first weight value, and the second weight value, wherein an ending time of the first time period is earlier than a starting time of the second time period.
10. A non-volatile storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, the storage medium is controlled by a device to execute the method for determining the item to be recommended according to any one of claims 1 to 8.
11. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of determining an item to be recommended according to any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116562960A (en) * | 2023-04-19 | 2023-08-08 | 上海聚灵兽科技有限公司 | Commodity recommendation method, equipment and storage medium |
| CN118628210A (en) * | 2024-08-07 | 2024-09-10 | 山东新浪潮传媒有限公司 | Communication service platform data enhancement method based on artificial intelligence |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116562960A (en) * | 2023-04-19 | 2023-08-08 | 上海聚灵兽科技有限公司 | Commodity recommendation method, equipment and storage medium |
| CN118628210A (en) * | 2024-08-07 | 2024-09-10 | 山东新浪潮传媒有限公司 | Communication service platform data enhancement method based on artificial intelligence |
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