CN114357293A - Object recommendation method and device, electronic equipment and computer-readable storage medium - Google Patents
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Abstract
The application provides an object recommendation method, an object recommendation device, electronic equipment and a computer-readable storage medium; the method comprises the following steps: acquiring attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects; determining a target reference object having attribute association with the object to be recommended from the plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each of the plurality of reference objects; obtaining scoring information of a target user aiming at a target reference object; predicting the score of a target user for the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of a target reference object to obtain a corresponding first predicted score; determining whether the object to be recommended meets the recommendation condition or not based on the first prediction score; and when the object to be recommended meets the recommendation condition, recommending the object to be recommended to the target user. By the method and the device, the recommended object can better accord with the preference of the user.
Description
Technical Field
The present application relates to computer technologies, and in particular, to an object recommendation method and apparatus, an electronic device, and a computer-readable storage medium.
Background
In the existing object-oriented recommendation method, more or less evaluation information of a user on an object is generally needed to be used for recommendation based on average information of the user, however, the problem of data sparsity in a recommendation algorithm is solved in the method, the recommendation of a completely new online object is difficult to realize, and for the online object, the user evaluation cannot well meet the user preference.
Disclosure of Invention
The embodiment of the application provides an object recommendation method, an object recommendation device, electronic equipment and a computer-readable storage medium, which can enable a recommended object to better accord with the preference of a user.
The technical scheme of the embodiment of the application is realized as follows:
the embodiment of the application provides an object recommendation method, which comprises the following steps:
acquiring attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects;
determining a target reference object having attribute association with the object to be recommended from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each of the plurality of reference objects;
obtaining scoring information of a target user for the target reference object;
predicting the score of the target user aiming at the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first predicted score;
determining whether the object to be recommended meets a recommendation condition based on the first prediction score;
and recommending the object to be recommended to the target user when the object to be recommended meets a recommendation condition.
In the foregoing solution, the predicting the score of the target user for the object to be recommended based on the score information, the attribute information of the object to be recommended, and the attribute information of the target reference object includes: determining the number of attributes of the target reference object based on the attribute information of the target reference object; determining the number of the same attributes between the target reference object and the object to be recommended; and predicting the score of the target user for the object to be recommended based on the scoring information, the number of the attributes of the target reference object and the number of the same attributes.
In the foregoing solution, the determining, based on the first prediction score, whether the object to be recommended meets the recommendation condition includes: when the object to be recommended does not have the scoring information of the user, obtaining the scoring time of the target user for the target reference object; determining a second prediction score of the target user for the object to be recommended based on the scoring time and the release time of the target reference object; and determining whether the object to be recommended meets a recommendation condition based on the first prediction score and the second prediction score.
In the foregoing solution, the determining, based on the scoring time and the release time of the target reference object, a second prediction score of the target user for the object to be recommended includes: respectively determining a time interval between the scoring time of the target user for each target reference object and the publishing time of the corresponding target reference object; summing the time intervals corresponding to the plurality of target reference objects to obtain the sum of the time intervals; determining the number of the target reference objects, and taking the sum of the number of the target reference objects and the time interval as the second prediction score.
In the foregoing solution, the determining whether the object to be recommended meets the recommendation condition based on the first prediction score and the second prediction score includes: determining a sum of the first predictive score and the second predictive score; when the score sum is greater than or equal to a score threshold value, determining that the object to be recommended meets a recommendation condition; and when the score sum is smaller than a score threshold value, determining that the object to be recommended does not meet the recommendation condition.
In the foregoing solution, the determining whether the object to be recommended meets the recommendation condition based on the first prediction score includes: when the object to be recommended has the rating information of the user, obtaining an evaluation reference object evaluated by the target user; obtaining the number of users of a plurality of other users who evaluate the evaluation reference object and the number of reference objects evaluated by each other user in the plurality of other users; the plurality of other users do not include the target user; determining a third prediction score of the target user for the object to be recommended based on the number of the users of the other users and the number of the reference objects evaluated by each other user; and determining whether the object to be recommended meets a recommendation condition based on the first prediction score and the third prediction score.
In the foregoing solution, the determining whether the object to be recommended meets the recommendation condition based on the first prediction score and the third prediction score includes: obtaining a first weight corresponding to the first prediction score and a second weight corresponding to the third prediction score; based on the first weight and the second weight, carrying out weighted summation on the first prediction score and the third prediction score to obtain a corresponding target prediction score; and determining whether the object to be recommended meets a recommendation condition or not based on the target prediction score.
An embodiment of the present application provides an object recommendation device, including:
the device comprises a first obtaining module, a second obtaining module and a recommendation module, wherein the first obtaining module is used for obtaining attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects;
the target reference object determining module is used for determining a target reference object which is associated with the object to be recommended in an attribute mode from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects;
the second obtaining module is used for obtaining the scoring information of the target user aiming at the target reference object;
the score prediction module is used for predicting the score of the target user for the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first prediction score;
the determining module is used for determining whether the object to be recommended meets a recommendation condition based on the first prediction score;
and the recommending module is used for recommending the object to be recommended to the target user when the object to be recommended meets the recommending condition.
An embodiment of the present application provides an electronic device, including:
a memory for storing executable instructions;
and the processor is used for realizing the object recommendation method provided by the embodiment of the application when the executable instructions stored in the memory are executed.
The embodiment of the application provides a computer-readable storage medium, which stores executable instructions for causing a processor to execute the method for recommending an object provided by the embodiment of the application.
The embodiment of the application has the following beneficial effects:
acquiring attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects; determining a target reference object having attribute association with the object to be recommended from the plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each of the plurality of reference objects; obtaining scoring information of a target user aiming at a target reference object; predicting the score of a target user for the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of a target reference object to obtain a corresponding first predicted score; determining whether the object to be recommended meets the recommendation condition or not based on the first prediction score; when the object to be recommended meets the recommendation condition, the object to be recommended is recommended to the target user, so that the recommended object can better accord with the preference of the user.
Drawings
FIG. 1 is an alternative structural diagram of an object recommendation system architecture provided in an embodiment of the present application;
fig. 2 is an alternative structural schematic diagram of an electronic device 200 provided in the embodiment of the present application;
FIG. 3 is an alternative flowchart of an object recommendation method according to an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating an alternative refinement of step 304 provided by an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating an alternative refinement of step 305 provided by an embodiment of the present application;
fig. 6 is a schematic diagram of an optional detailed flow of step 305 provided in this embodiment of the present application.
Detailed Description
In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
In the following description, references to the terms "first \ second \ third" are only to distinguish similar objects and do not denote a particular order, but rather the terms "first \ second \ third" are used to interchange specific orders or sequences, where appropriate, so as to enable the embodiments of the application described herein to be practiced in other than the order shown or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
Based on this, embodiments of the present application provide an object recommendation method, apparatus, electronic device, and computer-readable storage medium, which can make a recommended object better conform to user preferences.
First, an object recommendation system provided in an embodiment of the present application is described, referring to fig. 1, where fig. 1 is an optional architecture schematic diagram of an object recommendation system 100 provided in an embodiment of the present application, and a terminal 103 is connected to a server 101 through a network 102. In some embodiments, the terminal 103 may be, but is not limited to, a laptop, a tablet, a desktop computer, a smart phone, a dedicated messaging device, a portable gaming device, a smart speaker, a smart watch, and the like. The server 101 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN) service, and a big data and artificial intelligence platform. The network 102 may be a wide area network or a local area network, or a combination of both. The terminal 103 and the server 101 may be directly or indirectly connected through wired or wireless communication, and the embodiment of the present application is not limited thereto.
Next, an electronic device for implementing the object recommendation method according to an embodiment of the present application is described, referring to fig. 2, fig. 2 is an optional schematic structural diagram of an electronic device 200 according to an embodiment of the present application, and in practical applications, the electronic device 200 may be implemented as the terminal 103 or the server 101 in fig. 1, and the electronic device implementing the object recommendation method according to the embodiment of the present application is described by taking the electronic device as the terminal 103 shown in fig. 1 as an example. The electronic device 200 shown in fig. 2 includes: at least one processor 201, memory 205, at least one network interface 202, and a user interface 203. The various components in the electronic device 200 are coupled together by a bus system 204. It is understood that the bus system 204 is used to enable communications among the components. The bus system 204 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 204 in fig. 2.
The Processor 201 may be an integrated circuit chip having Signal processing capabilities, such as a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like, wherein the general purpose Processor may be a microprocessor or any conventional Processor, or the like.
The user interface 203 includes one or more output devices 2031, including one or more speakers and/or one or more visual display screens, that enable the presentation of media content. The user interface 203 also includes one or more input devices 2032 including user interface components that facilitate user input, such as a keyboard, mouse, microphone, touch screen display, camera, other input buttons and controls.
The memory 205 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid state memory, hard disk drives, optical disk drives, and the like. Memory 205 may optionally include one or more storage devices physically located remote from processor 201.
The memory 205 includes either volatile memory or nonvolatile memory, and may also include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read Only Memory (ROM), and the volatile Memory may be a Random Access Memory (RAM). The memory 205 described in embodiments herein is intended to comprise any suitable type of memory.
In some embodiments, the memory 205 is capable of storing data, examples of which include programs, modules, and data structures, or a subset or superset thereof, in support of various operations, in embodiments of the present application, the memory 205 has stored therein an operating system 2051, a network communication module 2052, a presentation module 2053, an input processing module 2054, and an object recommendation device 2055; in particular, the amount of the solvent to be used,
an operating system 2051, which includes system programs for handling various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and for handling hardware-based tasks;
a network communication module 2052 for communicating to other computing devices via one or more (wired or wireless) network interfaces 202, exemplary network interfaces 202 including: bluetooth, wireless compatibility authentication (WiFi), and Universal Serial Bus (USB), etc.;
a presentation module 2053 for enabling presentation of information (e.g., user interfaces for operating peripherals and displaying content and information) via one or more output devices 2031 (e.g., display screens, speakers, etc.) associated with the user interface 203;
an input processing module 2054 for detecting one or more user inputs or interactions from one of the one or more input devices 2032 and for translating the detected inputs or interactions.
In some embodiments, the object recommendation device provided in the embodiments of the present application may be implemented in software, and fig. 2 illustrates an object recommendation device 2055 stored in the memory 205, which may be software in the form of programs, plug-ins, and the like, and includes the following software modules: the first obtaining module 20551, the target reference object determining module 20552, the second obtaining module 20553, the score predicting module 20554, the determining module 20555 and the recommending module 20556 are logical and thus may be arbitrarily combined or further divided according to the functions implemented. The functions of the respective modules will be explained below.
In other embodiments, the object recommendation Device provided in this embodiment of the present Application may be implemented in hardware, and as an example, the object recommendation Device provided in this embodiment of the present Application may be a processor in the form of a hardware decoding processor, which is programmed to execute the object recommendation method provided in this embodiment of the present Application, for example, the processor in the form of the hardware decoding processor may employ one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), Field Programmable Gate Arrays (FPGAs), or other electronic components.
The object recommendation method provided by the embodiment of the present application will be described in conjunction with exemplary applications and implementations of the terminal provided by the embodiment of the present application.
Referring to fig. 3, fig. 3 is an alternative flowchart of an object recommendation method provided in an embodiment of the present application, and will be described with reference to the steps shown in fig. 3.
301, obtaining attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects;
303, obtaining scoring information of a target user aiming at the target reference object;
and step 306, recommending the object to be recommended to the target user when the object to be recommended meets a recommendation condition.
In an actual scenario, the object to be recommended and the reference object related to the embodiment of the present application are the same type of object, for example, but not limited to, video, music, and goods. Wherein the video may be, but is not limited to, a television show or a movie. The attribute information of the object includes a plurality of attributes, and the attributes may be, but are not limited to, the name of the object, the distribution time, the origin information (e.g., country of production or production), the object classification, and the like. For example, when the object is a movie item, the attribute information of the object may include, but is not limited to, a movie name, a release year (i.e., release time), a subject (i.e., object classification), a country (and origin information of the object), a duration, and the like.
Here, the attribute information of the object to be recommended and the attribute information of the plurality of reference objects may be stored in the server, or may be stored in an external storage device, such as a database, communicatively connected to the server. In actual implementation, the server obtains the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects from the local or external storage device. Then, a target reference object having an attribute association with the object to be recommended is determined from the plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each of the plurality of reference objects. Here, having an attribute association with the object to be recommended means that the object to be recommended has at least one same attribute. In some embodiments, having an attribute association with the object to be recommended may also mean that the attribute information has similarity with the attribute information of the object to be recommended.
In the embodiment of the application, the server compares the attribute information of the object to be recommended with the attribute information of the plurality of reference objects respectively to obtain a target reference object which is associated with the object to be recommended in the plurality of reference objects. Here, the number of target reference objects is at least two.
Then, the server obtains the scoring information of at least two target users aiming at the target reference object. Here, the scoring information includes a score value of the target user with respect to the target reference object. It should be understood that when the target user does not score a target reference object, the server cannot obtain the scoring information of the target reference object. In actual implementation, after obtaining the scoring information of the target reference objects, the server predicts the scoring of the target user for the object to be recommended based on the scoring information of each target reference object, the attribute information of the object to be recommended and the attribute information of the target reference object, and obtains corresponding first prediction scoring.
Specifically, referring to fig. 4, fig. 4 is an optional detailed flowchart of step 304 provided in this embodiment of the application, and step 304 may also be implemented as follows:
In actual implementation, the server counts the number of attributes of each target reference object based on the attribute information of each target reference object. And then, comparing the attribute information of the target reference object with that of the object to be recommended, determining the same attributes of the target reference object and the object to be recommended, and counting the number of the same attributes. The server determines a first prediction score of the target user for the object to be recommended based on the scoring information, the number of attributes of the target reference object and the number of the same attributes. In one embodiment, the server determines a first prediction score f of a target user u for an object to be recommended according to formula (1)a(u)：
Wherein, ACjIndicates the number of attributes, IAC, that the target reference object j haslRepresenting the number of target reference objects having an attribute l in common with the object to be recommended; when the target user u evaluates the target reference object j, r isuj1, otherwise, ruj0; when the target reference object j has an attribute of l, then hlj1, otherwise, hlj0; p is the number of attributes of the object to be recommended, and n is the number of target reference objects.
Based on the method, the association between the object to be recommended and the plurality of target reference objects is determined according to the scoring information of the target user on each target reference object, the attribute information of the object to be recommended, the attribute information of each target reference object, the number of attributes of each target reference object, and the number of target reference objects having common attributes with the object to be recommended, so that a first prediction score of the target user for the object to be recommended is obtained.
Then, the server determines whether the object to be recommended meets the recommendation condition based on the first prediction score. Here, the server may determine whether the object to be recommended satisfies the recommendation condition by determining whether the first prediction score reaches a first score threshold. And when the first prediction score reaches a first score threshold value, determining that the object to be recommended meets the recommendation condition, and when the first prediction score does not reach the first score threshold value, determining that the object to be recommended does not meet the recommendation condition.
In some embodiments, the attribute information of the reference object includes a publication time of the reference object. Referring to fig. 5, fig. 5 is a schematic view of an optional detailed flow of step 305 provided in an embodiment of the present application, where the step 305 may also be implemented by:
In an actual scene, an object to be recommended to a target user may be a newly issued object, and no scoring information of any user exists. In the embodiment of the application, whether the object to be recommended meets the recommendation condition is further determined by combining the scoring time of the target user for the target reference and the first prediction score. Specifically, the server obtains the scoring time of the target user for the target reference object, and determines a second prediction score of the target user for the object to be recommended based on the scoring time and the release time of the target reference object.
Here, the number of the target reference objects is plural. In some embodiments, step 502 may also be implemented by: respectively determining a time interval between the scoring time of the target user for each target reference object and the publishing time of the corresponding target reference object; summing the time intervals corresponding to the plurality of target reference objects to obtain the sum of the time intervals; determining the number of the target reference objects, and taking the sum of the number of the target reference objects and the time interval as the second prediction score.
Specifically, the server may determine a second prediction score of the target user u for the object to be recommended according to formula (2):
wherein (time)ui-datei) The smaller the value of (a), wuThe larger the distance between the time of evaluating the item and the time of releasing the item, the more actively the user likes to pay attention to the new object, and conversely, the timeui-datei) The larger the value of (A), wuThe smaller, the more negative the user is, the better to go to focus on items that have already been focused or rated by many users. Equation (2) reflects the user's preference for evaluating the average degree of new release objects. In recommending new items, both active and passive users may exist, but the preference is to recommend new items to the active users.
In some embodiments, step 503 may also be implemented by: determining a sum of the first predictive score and the second predictive score; when the score sum is greater than or equal to a score threshold value, determining that the object to be recommended meets a recommendation condition; and when the score sum is smaller than a score threshold value, determining that the object to be recommended does not meet the recommendation condition.
In actual implementation, the server sums the first prediction score and the second prediction score to obtain a score sum
Based on the obtained scores and
to determine whether the object to be recommended meets the recommendation condition.
In some embodiments, referring to fig. 6, fig. 6 is an optional detailed flowchart of step 305 provided in the embodiments of the present application, and step 305 may also be implemented by:
When the object to be recommended has the rating information of the user, the rating prediction of the target user is performed on the object to be recommended by combining the evaluation information of the target user on the target reference object to obtain a corresponding third prediction rating, and whether the object to be recommended meets the recommendation condition is determined by combining the first prediction rating and the third prediction rating. Specifically, the server may determine a third prediction score of the target user u for the object to be recommended according to formula (3):
wherein f isu(u) represents the predicted value of the user u's score for the target reference object j. If user u evaluates item j, then ruj1 is ═ 1; otherwise, ruj0. If user k evaluates item j, then rkj1 is ═ 1; otherwise, rkj＝0。ICkIndicating the number of items evaluated by user k, UCjThe number of users who evaluate the common evaluation item j is shown. Then, according to the rating information of the user for the item, the number of the user rating the item and the number of times the item is rated by the user, a prediction rating value based on the rating information of the item may be defined, thereby mining the relationship between them.
In some embodiments, the step 604 may also be implemented as follows: obtaining a first weight corresponding to the first prediction score and a second weight corresponding to the third prediction score; based on the first weight and the second weight, carrying out weighted summation on the first prediction score and the third prediction score to obtain a corresponding target prediction score; and determining whether the object to be recommended meets a recommendation condition or not based on the target prediction score.
Specifically, the server may calculate a weighted sum between the first prediction score and the third prediction score by equation (4):
wherein; UCjRepresenting the number of users of the common evaluation item j; w is auIs the temporal weight of user u. If item j is a new item, UCj0; otherwise; UCjNot equal to 0. Whether the item is a new item or an item which is already evaluated, the item has the attribute information of the item, and on the other hand; the user time weight information refers to the time interval between the user scoring time and the project release time; it does not matter whether the item is a new item; but the user time weight information can directly reflect the preference degree of the user for the item from another angle.
In the embodiment of the application, the attribute information of the object to be recommended and the attribute information of each reference object in a plurality of reference objects are obtained; determining a target reference object having attribute association with the object to be recommended from the plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each of the plurality of reference objects; obtaining scoring information of a target user aiming at a target reference object; predicting the score of a target user for the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of a target reference object to obtain a corresponding first predicted score; determining whether the object to be recommended meets the recommendation condition or not based on the first prediction score; when the object to be recommended meets the recommendation condition, the object to be recommended is recommended to the target user, so that the recommended object can better accord with the preference of the user.
Continuing with the exemplary structure of the object recommendation device 555 implemented as a software module provided in the embodiments of the present application, in some embodiments, as shown in fig. 2, the software modules stored in the object recommendation device 20551 of the memory 2055 may include:
the device comprises a first obtaining module, a second obtaining module and a recommendation module, wherein the first obtaining module is used for obtaining attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects;
the target reference object determining module is used for determining a target reference object which is associated with the object to be recommended in an attribute mode from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects;
the second obtaining module is used for obtaining the scoring information of the target user aiming at the target reference object;
the score prediction module is used for predicting the score of the target user for the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first prediction score;
the determining module is used for determining whether the object to be recommended meets a recommendation condition based on the first prediction score;
and the recommending module is used for recommending the object to be recommended to the target user when the object to be recommended meets the recommending condition.
In some embodiments, the score prediction module is further configured to: determining the number of attributes of the target reference object based on the attribute information of the target reference object; determining the number of the same attributes between the target reference object and the object to be recommended; and predicting the score of the target user for the object to be recommended based on the scoring information, the number of the attributes of the target reference object and the number of the same attributes.
In some embodiments, the attribute information of the reference object includes a publication time of the reference object, and the determining module is further configured to: when the object to be recommended does not have the scoring information of the user, obtaining the scoring time of the target user for the target reference object; determining a second prediction score of the target user for the object to be recommended based on the scoring time and the release time of the target reference object; and determining whether the object to be recommended meets a recommendation condition based on the first prediction score and the second prediction score.
In some embodiments, the number of the target reference objects is plural, and the score prediction module is further configured to: respectively determining a time interval between the scoring time of the target user for each target reference object and the publishing time of the corresponding target reference object; summing the time intervals corresponding to the plurality of target reference objects to obtain the sum of the time intervals; determining the number of the target reference objects, and taking the sum of the number of the target reference objects and the time interval as the second prediction score.
In some embodiments, the determining module is further configured to: determining a sum of the first predictive score and the second predictive score; when the score sum is greater than or equal to a score threshold value, determining that the object to be recommended meets a recommendation condition; and when the score sum is smaller than a score threshold value, determining that the object to be recommended does not meet the recommendation condition.
In some embodiments, the determining module is further configured to: when the object to be recommended has the rating information of the user, obtaining an evaluation reference object evaluated by the target user; obtaining the number of users of a plurality of other users who evaluate the evaluation reference object and the number of reference objects evaluated by each other user in the plurality of other users; the plurality of other users do not include the target user; determining a third prediction score of the target user for the object to be recommended based on the number of the users of the other users and the number of the reference objects evaluated by each other user; and determining whether the object to be recommended meets a recommendation condition based on the first prediction score and the third prediction score.
In some embodiments, the determining module is further configured to: obtaining a first weight corresponding to the first prediction score and a second weight corresponding to the third prediction score; based on the first weight and the second weight, carrying out weighted summation on the first prediction score and the third prediction score to obtain a corresponding target prediction score; and determining whether the object to be recommended meets a recommendation condition or not based on the target prediction score.
Embodiments of the present application provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the object recommendation method described in the embodiment of the present application.
Embodiments of the present application provide a computer-readable storage medium storing executable instructions, which when executed by a processor, cause the processor to perform the method provided by embodiments of the present application.
In some embodiments, the computer-readable storage medium may be memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash, magnetic surface memory, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories.
In some embodiments, executable instructions may be written in any form of programming language (including compiled or interpreted languages), in the form of programs, software modules, scripts or code, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
By way of example, executable instructions may correspond, but do not necessarily have to correspond, to files in a file system, and may be stored in a portion of a file that holds other programs or data, such as in one or more scripts in a hypertext Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).
By way of example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or distributed across multiple sites and interconnected by a communication network.
In summary, the object recommended to the user can better meet the user preference through the embodiment of the application.
The above description is only an example of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, and improvement made within the spirit and scope of the present application are included in the protection scope of the present application.
Claims (10)
1. An object recommendation method, comprising:
acquiring attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects;
determining a target reference object having attribute association with the object to be recommended from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each of the plurality of reference objects;
obtaining scoring information of a target user for the target reference object;
predicting the score of the target user aiming at the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first predicted score;
determining whether the object to be recommended meets a recommendation condition based on the first prediction score;
and recommending the object to be recommended to the target user when the object to be recommended meets a recommendation condition.
2. The object recommendation method according to claim 1, wherein the predicting the score of the target user for the object to be recommended based on the score information, the attribute information of the object to be recommended, and the attribute information of the target reference object comprises:
determining the number of attributes of the target reference object based on the attribute information of the target reference object;
determining the number of the same attributes between the target reference object and the object to be recommended;
and predicting the score of the target user for the object to be recommended based on the scoring information, the number of the attributes of the target reference object and the number of the same attributes.
3. The object recommendation method according to claim 1, wherein the attribute information of the reference object includes a publication time of the reference object, and the determining whether the object to be recommended satisfies the recommendation condition based on the first prediction score includes:
when the object to be recommended does not have the scoring information of the user, obtaining the scoring time of the target user for the target reference object;
determining a second prediction score of the target user for the object to be recommended based on the scoring time and the release time of the target reference object;
and determining whether the object to be recommended meets a recommendation condition based on the first prediction score and the second prediction score.
4. The object recommendation method according to claim 3, wherein the number of the target reference objects is multiple, and the determining a second prediction score of the target user for the object to be recommended based on the score time and the release time of the target reference object comprises:
respectively determining a time interval between the scoring time of the target user for each target reference object and the publishing time of the corresponding target reference object;
summing the time intervals corresponding to the plurality of target reference objects to obtain the sum of the time intervals;
determining the number of the target reference objects, and taking the sum of the number of the target reference objects and the time interval as the second prediction score.
5. The object recommendation method according to claim 3, wherein the determining whether the object to be recommended meets a recommendation condition based on the first prediction score and the second prediction score comprises:
determining a sum of the first predictive score and the second predictive score;
when the score sum is greater than or equal to a score threshold value, determining that the object to be recommended meets a recommendation condition;
and when the score sum is smaller than a score threshold value, determining that the object to be recommended does not meet the recommendation condition.
6. The object recommendation method according to claim 1, wherein the determining whether the object to be recommended meets a recommendation condition based on the first prediction score comprises:
when the object to be recommended has the rating information of the user, obtaining an evaluation reference object evaluated by the target user;
obtaining the number of users of a plurality of other users who evaluate the evaluation reference object and the number of reference objects evaluated by each other user in the plurality of other users; the plurality of other users do not include the target user;
determining a third prediction score of the target user for the object to be recommended based on the number of the users of the other users and the number of the reference objects evaluated by each other user;
and determining whether the object to be recommended meets a recommendation condition based on the first prediction score and the third prediction score.
7. The object recommendation method according to claim 6, wherein the determining whether the object to be recommended meets a recommendation condition based on the first prediction score and the third prediction score comprises:
obtaining a first weight corresponding to the first prediction score and a second weight corresponding to the third prediction score;
based on the first weight and the second weight, carrying out weighted summation on the first prediction score and the third prediction score to obtain a corresponding target prediction score;
and determining whether the object to be recommended meets a recommendation condition or not based on the target prediction score.
8. An object recommendation apparatus, comprising:
the device comprises a first obtaining module, a second obtaining module and a recommendation module, wherein the first obtaining module is used for obtaining attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects;
the target reference object determining module is used for determining a target reference object which is associated with the object to be recommended in an attribute mode from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects;
the second obtaining module is used for obtaining the scoring information of the target user aiming at the target reference object;
the score prediction module is used for predicting the score of the target user for the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first prediction score;
the determining module is used for determining whether the object to be recommended meets a recommendation condition based on the first prediction score;
and the recommending module is used for recommending the object to be recommended to the target user when the object to be recommended meets the recommending condition.
9. An electronic device, comprising:
a memory for storing executable instructions;
a processor for implementing the object recommendation method of any one of claims 1 to 7 when executing executable instructions stored in the memory.
10. A computer-readable storage medium storing executable instructions for implementing the object recommendation method of any one of claims 1 to 7 when executed by a processor.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115136163A (en) * | 2022-05-25 | 2022-09-30 | 广东逸动科技有限公司 | Management method, management apparatus, electronic device, and computer-readable storage medium |
| CN115136163B (en) * | 2022-05-25 | 2023-08-18 | 广东逸动科技有限公司 | Management method, management device, electronic apparatus, and computer-readable storage medium |
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