CN114510185A - Virtual object recommendation method, medium, device and computing equipment - Google Patents
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Abstract
The virtual object recommendation method, medium, device and computing equipment provided by the embodiment of the disclosure are applied to the technical field of internet. When virtual object recommendation is performed for a user, responding to a trigger instruction for displaying a display interface containing a virtual object, and acquiring first image information of a target media object, wherein the target media object is determined according to user characteristic data; determining a degree of correlation of the first image information and a set of second image information of the at least one virtual object; determining a target virtual object recommended for the user according to the correlation; and recommending the target virtual object to the user. In the embodiment of the disclosure, the relevancy of the set of the first image information of the target media object and the second image information of the at least one virtual object is determined by combining with the target media object determined according to the user characteristic data, and then the target virtual object recommended for the user is determined according to the relevancy so as to meet the personalized demand of the user.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of internet, in particular to a method, a medium, a device and a computing device for recommending a virtual object.
Background
This section is intended to provide a background or context to the embodiments of the disclosure recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
With the continuous development of internet products, people have higher and higher interactive demands on internet products, for example, in media platforms such as music playing platforms and video playing platforms, if personalized interactive modes can be provided to share virtual objects (for example, membership cards and gift cards with different card surface images), user experience can be significantly improved.
At present, when virtual object recommendation is performed for a user, a virtual object with a higher temperature in some fixed virtual objects is generally recommended, and personalized requirements of the user cannot be met.
Disclosure of Invention
The disclosure provides a recommendation method, medium, device and computing equipment for virtual objects, so as to meet personalized requirements of users and improve user experience.
In a first aspect of the disclosed embodiments, a method for recommending a virtual object is provided, including: responding to a trigger instruction for displaying a display interface containing a virtual object, and acquiring first image information of a target media object, wherein the target media object is determined according to user characteristic data; determining a degree of correlation of the first image information and a set of second image information of the at least one virtual object; determining a target virtual object recommended for the user according to the correlation; and recommending the target virtual object to the user.
In one embodiment of the disclosure, the media object may include a song, the first image information may include a song cover and a song cover, the second image information may include a virtual object cover, and the correlation may include a histogram chi-squared coefficient and a histogram intersection coefficient. Correspondingly, the determining the correlation between the first image information and the set of second image information of the at least one virtual object may include: determining a relevance of a song cover to a set of virtual object covers of at least one virtual object; a relevance of the music style cover to a set of virtual object covers of at least one virtual object is determined.
In an embodiment of the present disclosure, the determining, according to the correlation, a target virtual object recommended for the user may include: and determining the virtual object corresponding to the relevancy meeting the preset relevancy rule in the at least one virtual object as a target virtual object recommended to the user.
In one embodiment of the present disclosure, the method for recommending a virtual object may further include: before determining the correlation degree, responding to that at least one of the song cover, the song style cover and the virtual object cover is an RGB image, and carrying out gray processing on the RGB image to obtain a gray image.
In an embodiment of the present disclosure, the determining, according to the correlation, a target virtual object recommended for the user may include: acquiring user characteristic data; inputting the user characteristic data and the relevancy into a recommendation model for processing to obtain a recommendation value of the virtual object when the virtual object faces the user; and determining the virtual object with the recommendation value meeting the recommendation condition as a target virtual object.
In an embodiment of the present disclosure, the recommendation model is obtained by:
acquiring a training data set, wherein the training data set comprises a plurality of training samples, and the training samples comprise virtual object covers, song style covers and user characteristic data;
determining the correlation degree of a song cover and a virtual object cover in the training sample and the correlation degree of the song cover and the virtual object cover according to the training sample in the training data set; and training the recommendation model according to the similarity corresponding to the training samples and the user characteristic data to obtain the trained recommendation model.
In one embodiment of the present disclosure, the user characteristic data may include interaction data of the user facing the media object, basic data of the user, and related data of the user facing the media platform, etc. The interactive data may include a playing time length and/or a playing frequency, etc.; the profile may include age and/or gender, etc.; the related data may include whether or not it is at least one of member, platform grade information, virtual object actual purchase information, and member grade information.
In one embodiment of the present disclosure, the method for recommending a virtual object may further include: and responding to the historical interactive data facing the target virtual object in the set duration, and updating the recommendation model according to the virtual object cover of the target virtual object and the first image information to obtain the updated recommendation model.
In a second aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, implement a method for recommending a virtual object as in the first aspect.
In a third aspect of the disclosed embodiments, there is provided an apparatus for recommending a virtual object, comprising:
the acquisition module is used for responding to a trigger instruction for displaying a display interface containing a virtual object, and acquiring first image information of a target media object, wherein the target media object is determined according to user characteristic data;
a first determining module for determining a degree of correlation of the first image information and a set of second image information of the at least one virtual object;
the second determination module is used for determining a target virtual object recommended for the user according to the correlation;
and the recommending module is used for recommending the target virtual object to the user.
In one embodiment of the disclosure, the media object includes a song, the first image information includes a song cover and a song cover, the second image information includes a virtual object cover, and the correlation includes a histogram chi-squared coefficient and a histogram intersection coefficient. In this case, the first determining module may be specifically configured to: determining a relevance of a song cover to a set of virtual object covers of at least one virtual object; a relevance of the music style cover to a set of virtual object covers of at least one virtual object is determined.
In an embodiment of the disclosure, the second determining module may be specifically configured to: and determining the virtual object corresponding to the relevancy meeting the preset relevancy rule in the at least one virtual object as a target virtual object recommended to the user.
In an embodiment of the disclosure, the second determining module may be specifically configured to: acquiring user characteristic data; inputting the user characteristic data and the relevancy into a recommendation model for processing to obtain a recommendation value of the virtual object when the virtual object faces the user; and determining the virtual object with the recommendation value meeting the recommendation condition as a target virtual object.
In an embodiment of the present disclosure, the recommendation model is obtained by:
acquiring a training data set, wherein the training data set comprises a plurality of training samples, and the training samples comprise virtual object covers, song style covers and user characteristic data;
determining the correlation degree of a song cover and a virtual object cover in the training sample and the correlation degree of the song cover and the virtual object cover according to the training sample in the training data set; and training the recommendation model according to the similarity corresponding to the training samples and the user characteristic data to obtain the trained recommendation model.
In an embodiment of the present disclosure, the second determining module may be further configured to: and in response to the fact that historical interactive data facing the target virtual object exist in the set duration, updating the recommendation model according to the virtual object cover of the target virtual object and the first image information to obtain the updated recommendation model.
In a fourth aspect of embodiments of the present disclosure, there is provided a computing device comprising: at least one processor and memory; the memory stores computer-executable instructions; the at least one processor executes the memory-stored computer-executable instructions to cause the at least one processor to perform the method of recommending virtual objects as in the first aspect.
When virtual object recommendation is performed for a user, in response to a trigger instruction for displaying a display interface including a virtual object, first image information of a target media object is acquired, wherein the target media object is determined according to user characteristic data; determining a degree of correlation of the first image information and a set of second image information of the at least one virtual object; determining a target virtual object recommended for the user according to the correlation; and recommending the target virtual object to the user. In the embodiment of the disclosure, the relevancy of the set of the first image information of the target media object and the second image information of the at least one virtual object is determined by combining with the target media object determined according to the user characteristic data, and then the target virtual object recommended to the user is determined according to the relevancy, so that the personalized requirements of the user are met, and the user experience is improved.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present disclosure will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
fig. 1 is a schematic view of an application scenario provided in an embodiment of the present disclosure;
fig. 2 is a first flowchart illustrating a method for recommending a virtual object according to an embodiment of the present disclosure;
FIG. 3 is an exemplary diagram of an access display interface including virtual objects provided by an embodiment of the present disclosure;
fig. 4 is a flowchart illustrating a second method for recommending a virtual object according to an embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of a storage medium provided in an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a virtual object recommendation apparatus according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a computing device according to an embodiment of the present disclosure.
In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Detailed Description
The principles and spirit of the present disclosure will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given solely for the purpose of enabling those skilled in the art to better understand and to practice the present disclosure, and are not intended to limit the scope of the present disclosure in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
According to the embodiment of the disclosure, a recommendation method, a medium, a device and a computing device of a virtual object are provided.
Moreover, any number of elements in the drawings are by way of example and not by way of limitation, and any nomenclature is used solely for differentiation and not by way of limitation.
First, some terms related to the present disclosure are explained:
an Application (APP for short) refers to a program installed on a smart device such as a mobile phone, and generally needs to be operated in cooperation with a server. Common applications fall into two main categories: one is pre-installed system applications such as short messages, photos, memos, etc.; still another type is third party applications such as information type APP, shopping type APP, social type APP, etc.
The virtual object is, for example, a membership card, a gift card, or a membership gift card. For example, for a music platform, the member gift card may be an electronic card containing a cover made of an elegant image, and after the user purchases the music platform, the member gift card may be given to other users in the music platform, and after the recipient receives the member gift card, the member gift card will automatically become a member of the music platform.
User level, scenario example: the user level of the user in the music APP can be determined by the time length for logging in the music APP and the number of song listening heads of the user; the user rating may be characterized by a number, which may range from 0 to 10.
Membership grade, for example: the member level of the user in the music APP is determined by the growth value obtained after the user participates in a designated member task (such as listening member song, etc.). Membership grades may be characterised by numbers, which may range from 1 to 7.
Payment channels, currently mainly include third party payment institution channels: such as a WeChat payment or a Payment, etc.
RGB, a color standard in the industry, is obtained by changing three color channels of red (R), green (G) and blue (B) and superimposing them on each other to obtain various colors, and is a color representing three channels of red, green and blue.
The principles and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure.
Summary of The Invention
In view of the fact that the virtual object recommended to the user in the related art is relatively fixed and cannot meet the personalized requirements of the user, and user experience is poor, embodiments of the present disclosure provide a method, a medium, an apparatus, and a computing device for recommending a virtual object, in which the virtual object is recommended to the user by combining the correlation between the media object and the virtual object determined according to the user characteristic data, so that the personalized requirements of the user are met, and the user experience is improved.
Having described the general principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.
Application scene overview
Referring to fig. 1 first, fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present disclosure, where the device involved in the application scenario includes a terminal device 101 and a server 102.
The server 102 and the terminal device 101 may communicate with each other through a network.
The terminal device 101 may be a Personal Digital Assistant (PDA) device, a handheld device (e.g., a smart phone or a tablet computer) having a wireless communication function, a computing device (e.g., a Personal Computer (PC)), an in-vehicle device, a wearable device (e.g., a smart watch or a smart band), a smart home device (e.g., a smart display device), and the like.
The server 102 may be a product server of the application, and user data, business data, and the like of the application are deployed in the server 102, thereby providing services related to the application to users of a plurality of devices (e.g., the terminal device 101).
Illustratively, the server 102 is, for example, a music playing server or a video playing server, and correspondingly, the application program may be a music playing application program or a video playing application program, and so on.
Accordingly, the terminal apparatus 101 is a client installed with an application program, and a user can use a service provided in the server 102 through the client.
In this application scenario, the terminal device 101 sends a trigger instruction for displaying a display interface including a virtual object to the server 102, and the server 102 determines a target virtual object recommended by the user in response to the trigger instruction and recommends the target virtual object to the user, that is, sends the target virtual object to the terminal device 101, so that the terminal device 101 displays the target virtual object. For a specific implementation process of the server 102 determining the target virtual object, reference may be made to the following solutions of the embodiments.
It should be noted that fig. 1 is only a schematic diagram of an application scenario provided by the embodiment of the present disclosure, and the embodiment of the present disclosure does not limit the devices included in fig. 1, nor does it limit the positional relationship between the devices in fig. 1. For example, in the application scenario shown in fig. 1, a data storage device may be further included, and the data storage device may be an external memory with respect to the terminal device 101 or the server 102, or may be an internal memory integrated in the terminal device 101 or the server 102.
Exemplary method
A recommendation method of a virtual object according to an exemplary embodiment of the present disclosure is described below with reference to fig. 2 to 4 in conjunction with the application scenario of fig. 1. It should be noted that the above application scenarios are merely illustrated for the convenience of understanding the spirit and principles of the present disclosure, and the embodiments of the present disclosure are not limited in this respect. Rather, embodiments of the present disclosure may be applied to any scenario where applicable.
Referring to fig. 2, fig. 2 is a first flowchart illustrating a method for recommending a virtual object according to an embodiment of the present disclosure. As shown in fig. 2, the method for recommending a virtual object includes:
s201, responding to a trigger instruction for displaying a display interface containing a virtual object, and acquiring first image information of a target media object, wherein the target media object is determined according to user characteristic data.
In the process that the user uses the service provided in the server 102 through the terminal device 101, the user may cause the terminal device 101 to display a display interface including a virtual object through a plurality of ways, which is described as an example below, but the embodiment of the present disclosure is not specifically limited:
in one embodiment, a control for entering the display interface containing the virtual object may be provided in an interactive interface displayed by the terminal device 101, and a user may click the control to cause the terminal device 101 to jump from the current interactive interface to the display interface containing the virtual object. For example, still taking a music APP as an example, the user may enter the terminal device 101 into a display interface containing virtual objects by: as shown in fig. 3, the user opens the music APP installed in the terminal device 101 to enter a display interface B, and a control identified as a "gift card" is provided in the display interface B; the user clicks on "gift card" to access display interface C containing the gift card. In this example, the virtual object is a gift card. It should be noted that fig. 3 illustrates 2 gift cards (gift card 1 and gift card 2), but the present disclosure does not limit the number and arrangement of the gift cards in the display interface.
In another embodiment, the virtual object may be shared with the user in a sharing manner such as a link, and when the user clicks the link, the terminal device 101 may jump from the current interface to the display interface including the virtual object. The current interface may be any type of interface, such as an interface of an information APP or an interactive interface of a social APP, and the embodiment of the disclosure is not limited in particular. Through this embodiment, can further promote interactive effect, promote user experience.
For example, the trigger instruction for displaying the display interface including the virtual object is sent by the terminal device 101 to the server 102 in response to a user operation, and the user operation may be any one of a click operation, a double click operation, a long press operation, and the like, for example, the "click link" mentioned in the foregoing embodiment, and thus, the embodiment of the present disclosure is not particularly limited. Correspondingly, the server 102 receives the trigger instruction, responds to the trigger instruction, and executes step S201.
For a target media object, it is understood that the target media object corresponds to a media platform. For example, for the media platform being a music playing platform, the target media object may be a song; for the media platform being a video playing platform, the target media object may be a video; for media platforms that are literary work reading platforms, the target media object may be a literary work, and so on. And, the target media object is determined according to user characteristic data, such as a media object selected by the user or a song the user is listening to or an audio-visual or literary work the user is watching, etc.
In addition, the first image information corresponds to the target media object. For example, for a song, the first image information may be a song cover or a song cover; for video, the first image information may be a video cover or any video frame; for literary works, the first image information may be a cover of the literary work, and so on. By a song cover, it is understood a cover for presenting a song. The song songs may include, but are not limited to, hip hop, rock, pop, jazz, punk, classical, ghost, new century, metal, blues, latin, country music, electronic dance, ballad, etc.
S202, determining the correlation degree of the first image information and the set of second image information of the at least one virtual object.
It is understood that there is usually at least one virtual object, each virtual object corresponds to at least one second image information, such as a virtual object cover, and the second image information of the at least one virtual object forms a set, such as a second image information library or a second image information pool, etc.
In this step, the correlation between the first image information and the set of second image information of the at least one virtual object may be determined separately, or may be determined separately, and the disclosure is not limited specifically. In addition, when determining the degree of correlation between the first image information and the second image information of a virtual object, the degree of correlation between the first image information and a second image information of the virtual object may be determined; or determining the correlation between the first image information and the plurality of second image information of the virtual object, and further obtaining the correlation between the first image information and the second image information of the virtual object according to the plurality of correlations.
Illustratively, the correlation may include a histogram chi-squared coefficient and/or a histogram intersection coefficient, and the like. If the first image represented by the first image information and the second image represented by the second image information are both gray level images, the correlation degree of the first image information and the second image information can be obtained by calculating the histogram chi-square coefficient and/or the histogram intersection coefficient of the first image and the second image, and the correlation degree can reflect the correlation degree of the first image and the second image.
As an example, the calculation formula of the histogram chi-squared coefficients of two gray images:
a and b respectively represent histograms of two gray images, N is 255, i ranges from 0 to 255, and ai，biRespectively representing pixels on corresponding gray value iThe smaller the number of the chi-squared coefficients of the visible histogram, the higher the correlation between the two gray images.
As another example, a calculation formula of the histogram intersection coefficients of two grayscale images:
a, b represent histograms of two gray images, M is 256, i ranges from 0 to 255, ai,biRespectively representing the number of pixels on the corresponding gray value i, and the larger the intersection coefficient of the visible histograms is, the higher the correlation degree of the two gray images is.
Taking the correlation as the histogram chi-squared coefficient as an example, if the histogram chi-squared coefficients of the first image information and the 5 second image information of the virtual object V are d11，d12，d13，d14And d15Further, the histogram chi-squared coefficient of the first image information and the second image information of the virtual object V may be obtained from the 5 histogram chi-squared coefficients as (d)11+d12+d13+d14+d15)/5。
The "histogram of a gray image" mentioned in the above example can be understood as a statistical result of the number or frequency of occurrences of the gray value of each pixel in an image, which only reflects the frequency of occurrences of the gray value in the image, but not the position of the pixel of a certain gray value.
In some embodiments, if at least one of the first image and the second image is an RGB image, the correlation is determined after performing gray processing on the RGB image to obtain a gray image. For example, the conversion between the RGB image and the gray image, the gray value calculation formula of the gray image may be as follows: and the Grey is 0.299R + 0.587G + 0.114B, wherein R represents a red component, G represents a green component, and B represents a blue component, and the value of the Grey can range from 0 to 255.
In one embodiment of the present disclosure, the media object may include a song, the first image information may include a song cover and a song cover, the second image information may include a virtual object cover, and the correlation may include a histogram chi-squared coefficient and a histogram intersection coefficient. Correspondingly, the step may further comprise: determining a relevance of a song cover to a set of virtual object covers of at least one virtual object; a relevance of the music style cover to a set of virtual object covers of at least one virtual object is determined.
And S203, determining a target virtual object recommended for the user according to the correlation.
This step can be implemented in a variety of ways. For example:
in a first implementation, the step may further include: and determining the virtual object corresponding to the relevancy meeting the preset relevancy rule in the at least one virtual object as a target virtual object recommended to the user. The preset rule of the degree of correlation may be determined according to actual requirements and/or historical experience. For example, the preset rule of the degree of correlation may specifically be that the degree of correlation is greater than or equal to a threshold value of the degree of correlation, or the preset rule of the degree of correlation may specifically be that the degree of correlation ranks first by a first set number of degrees of correlation, and so on.
In a second implementation manner, the step may further include: acquiring user characteristic data; inputting the user characteristic data and the relevancy into a recommendation model for processing to obtain a recommendation value of the virtual object when the virtual object faces the user; and determining the virtual object with the recommendation value meeting the recommendation condition as a target virtual object. In this implementation, the recommendation condition is similar to the preset rule of the degree of correlation, for example, the recommendation condition may specifically be that the recommendation value is greater than or equal to the recommendation value threshold, or the recommendation condition may specifically be that the recommendation values are ranked in the first second set number of recommendation values, and the like, for example, the top 4 gift cards with the highest recommendation value calculated by the recommendation model are taken as the display card surfaces. The user characteristic data referred to in the present disclosure may be data authorized by the user or sufficiently authorized by each party.
Alternatively, the user characteristic data may comprise interaction data of the user facing the media object, basic data of the user and related data of the user facing the media platform, etc. The interactive data may include a playing time length and/or a playing frequency, etc.; the profile may include age and/or gender, etc.; the related data may include at least one of information on whether it is a member, platform level information, virtual object actual purchase information, and member level information.
According to the implementation mode, the information such as the correlation between the first image information of the target media object and the second image information of the virtual object, the platform grade information of the user, the member grade information of the user, the payment behavior of the user and the like is comprehensively analyzed and judged to determine the target virtual object suitable for the user, so that the recommendation can better meet the personalized requirements of the user, and the user experience is improved.
In one application example, based on the song listening behavior of a user in a music APP, determining a song played when entering a gift card purchase page as a target media object, and determining the correlation between a cover image of the song and a cover image of at least one gift card; and determining the gift card corresponding to the preset relevancy rule as the gift card recommended to the user, and selecting a cover image of the appropriate gift card to display to the user, so that the purchase possibility of the user is improved, and the sales volume of virtual objects such as the gift card is increased.
And S204, recommending the target virtual object to the user.
In the embodiment of the disclosure, when virtual object recommendation is performed for a user, in response to a trigger instruction for displaying a display interface including a virtual object, first image information of a target media object is acquired, wherein the target media object is determined according to user characteristic data; determining a degree of correlation of the first image information and a set of second image information of the at least one virtual object; determining a target virtual object recommended for the user according to the correlation; and recommending the target virtual object to the user. The method comprises the steps of determining the correlation degree of a set of first image information of a target media object and second image information of at least one virtual object by combining with the target media object determined according to user characteristic data, and further determining a target virtual object recommended for a user according to the correlation degree so as to meet the personalized requirements of the user and improve the user experience.
On the basis of the above embodiment, the recommendation model may be obtained by:
acquiring a training data set, wherein the training data set comprises a plurality of training samples, and the training samples comprise virtual object covers, song style covers and user characteristic data; determining the correlation degree of a song cover and a virtual object cover in the training sample and the correlation degree of the song cover and the virtual object cover according to the training sample in the training data set; and training the recommendation model according to the similarity corresponding to the training samples and the user characteristic data to obtain the trained recommendation model.
In this disclosure, the device for training the recommendation model and the device for applying the recommendation model may be the same device or different devices, and this disclosure is not limited thereto.
Before training the recommendation model, a training data set is also collected. Specifically, the virtual object data and the user feature data are integrated to form a data dimension table, and the data dimension table is determined to be a training data set, or the training data set is obtained according to the data dimension table.
Here, taking the virtual object as a gift card as an example, a specific implementation of integrating virtual object data and user characteristic data to form a data dimension table is described:
1) and initializing the cover data of the gift card, and converting the RGB images of all the cover surfaces of the gift card into grayscale images.
2) Acquiring a song identifier (such as a song name or a song ID) when a user enters a gift card purchasing page, determining a song cover, converting the song cover into a gray image, and calculating a histogram chi-square coefficient and a histogram intersection coefficient of the gray image and the gray image of each gift card cover.
3) And acquiring the song listening time of the song and the song listening times of the song on the current day.
4) The method comprises the steps of obtaining song music when a user enters a gift card purchasing page, obtaining a music cover, converting the music cover into a gray image, and calculating a histogram chi-square coefficient and a histogram intersection coefficient of the gray image and the gray image of each gift card cover.
5) And acquiring user characteristic data such as whether the user is a member, platform grade information, member grade information and the like.
When the recommendation model is trained, a neural network model can be adopted to construct positive and negative sample data for model training. When positive and negative sample data are constructed, based on a training data set, for example, if a user enters a gift card purchasing page from a song page and places a purchase order for the gift card, a result column of the data column is set to be 1; if the user enters a gift card purchasing page from a song page and clicks the gift card, setting the result column of the data column as 1; if not, the result column for this data column is set to 0. And after the positive and negative samples are cut, carrying out model training.
In the application process of the recommendation model, the media platform obtains relevant data of virtual objects browsed, clicked and actually traded by the user, and the recommendation model can be updated based on the relevant data. Referring to fig. 4, fig. 4 is a schematic flowchart illustrating a second method for recommending a virtual object according to an embodiment of the present disclosure. As shown in fig. 4, the method for recommending a virtual object may include:
s401, responding to a trigger instruction for displaying a display interface containing a virtual object, and acquiring first image information of a target media object, wherein the target media object is determined according to user characteristic data.
S402, determining the correlation degree of the first image information and the set of second image information of the at least one virtual object.
For specific implementation of S401 and S402, reference may be made to S201 and S202, which are not described herein again.
S203 above may be further refined to S403 to S405:
and S403, acquiring user characteristic data.
S404, inputting the user characteristic data and the correlation into a recommendation model for processing to obtain a recommendation value of the virtual object when the virtual object faces the user.
S405, determining the virtual object with the recommendation value meeting the recommendation condition as a target virtual object.
S406, recommending the target virtual object to the user.
S407, responding to the historical interaction data facing the target virtual object in the set duration, and updating the recommendation model according to the virtual object cover of the target virtual object and the first image information to obtain the updated recommendation model.
In this step, the historical interaction data may be specifically data of browsing, clicking and actual transaction of the target virtual object oriented by the user. Taking a target media object as a song and a target virtual object as a membership card as an example, if historical interactive data facing the target membership card exists after the target membership card is recommended for a user, updating a recommendation model according to data such as a membership card cover and a song cover of the target membership card to obtain an updated recommendation model, and replacing the currently applied recommendation model with the updated recommendation model to continuously optimize the recommendation model.
Through the continuous updating of the recommendation model, the virtual object recommended by the recommendation model can better meet the personalized requirements of the user, the user experience is further improved, and the user experience is improved.
Exemplary Medium
Having described the method of the exemplary embodiment of the present disclosure, next, a storage medium of the exemplary embodiment of the present disclosure will be described with reference to fig. 5.
Referring to fig. 5, a storage medium 500 stores a program product for implementing the above method according to an embodiment of the present disclosure, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a computing device, such as a server. However, the program product of the present disclosure is not limited thereto.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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 foregoing.
A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. The readable signal medium may also be any readable medium other than a readable storage medium.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN). For example, the program code executed by the computing device may be program code executed by a processor in the computing device to implement the recommendation method for a virtual object as described above.
Exemplary devices
Having described the medium of the exemplary embodiment of the present disclosure, next, a recommendation apparatus of a virtual object of the exemplary embodiment of the present disclosure will be explained with reference to fig. 6.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a virtual object recommendation apparatus according to an embodiment of the present disclosure. As shown in fig. 6, a recommendation apparatus 600 for a virtual object provided in an embodiment of the present disclosure includes:
an obtaining module 601, configured to obtain first image information of a target media object in response to a trigger instruction for displaying a display interface including a virtual object, where the target media object is determined according to user characteristic data;
a first determining module 602 for determining a degree of correlation of the first image information and the set of second image information of the at least one virtual object;
a second determining module 603, configured to determine, according to the correlation, a target virtual object recommended for the user;
a recommending module 604 for recommending the target virtual object to the user.
In one embodiment of the disclosure, the media object may include a song, the first image information may include a song cover and a song cover, the second image information may include a virtual object cover, and the correlation may include a histogram chi-squared coefficient and a histogram intersection coefficient. In this case, the first determining module 602 may specifically be configured to: determining a relevance of a song cover to a set of virtual object covers of at least one virtual object; a relevance of the music style cover to a set of virtual object covers of at least one virtual object is determined.
In an embodiment of the present disclosure, the second determining module 603 may be specifically configured to: and determining the virtual object corresponding to the relevancy meeting the preset relevancy rule in the at least one virtual object as a target virtual object recommended to the user.
In an embodiment of the present disclosure, the second determining module 603 may be specifically configured to: acquiring user characteristic data; inputting the user characteristic data and the relevancy into a recommendation model for processing to obtain a recommendation value of the virtual object when the virtual object faces the user; and determining the virtual object with the recommendation value meeting the recommendation condition as a target virtual object.
In one embodiment of the present disclosure, the recommendation model may be obtained by:
acquiring a training data set, wherein the training data set comprises a plurality of training samples, and the training samples comprise virtual object covers, song style covers and user characteristic data;
determining the correlation degree of a song cover and a virtual object cover in the training sample and the correlation degree of the song cover and the virtual object cover according to the training sample in the training data set; and training the recommendation model according to the similarity corresponding to the training samples and the user characteristic data to obtain the trained recommendation model.
In an embodiment of the present disclosure, the second determining module 603 may be further configured to: and in response to the fact that historical interactive data facing the target virtual object exist in the set duration, updating the recommendation model according to the virtual object cover of the target virtual object and the first image information to obtain the updated recommendation model.
It should be noted that the recommendation apparatus for a virtual object provided in the embodiment of the present disclosure is used to implement the recommendation method for a virtual object in any of the method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
Exemplary computing device
Having described the methods, media, and apparatus of the exemplary embodiments of the present disclosure, a computing device of the exemplary embodiments of the present disclosure is described next with reference to fig. 7. It should be understood that the computing device 700 shown in FIG. 7 is only one example and should not be taken as limiting the scope of use and functionality of embodiments of the disclosure.
Fig. 7 is a schematic structural diagram of a computing device according to an embodiment of the present disclosure. As shown in fig. 7, computing device 700 is embodied in the form of a general purpose computing device. Components of computing device 700 may include, but are not limited to: at least one processing unit 701, at least one memory unit 702, and a bus 703 that couples various system components including the processing unit 701 and the memory unit 702.
The processing unit 701 may include a processor. The processing unit 701 executes the computer-executable instructions stored in the storage unit 702 to perform the above-described recommendation method of the virtual object.
The storage unit 702 may include readable media in the form of volatile memory, such as Random Access Memory (RAM)712 and/or cache memory 722, and may further include readable media in the form of non-volatile memory, such as Read Only Memory (ROM) 732.
The bus 703 may include a data bus, a control bus, and an address bus.
The computing device 700 may also communicate with one or more external devices 704 (e.g., keyboard, pointing device, etc.). Such communication may occur via input/output (I/O) interfaces 705. Moreover, the computing device 700 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 706. As shown in FIG. 7, the network adapter 706 communicates with the other modules of the computing device 700 over the bus 703.
It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computing device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the recommendation device for virtual objects are mentioned, such division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module, in accordance with embodiments of the present disclosure. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Further, while the operations of the disclosed methods are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
While the spirit and principles of the present disclosure have been described with reference to several particular embodiments, it is to be understood that the present disclosure is not limited to the particular embodiments disclosed, nor is the division of aspects, which is for convenience only as the features in such aspects may not be combined to benefit. The disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A method of recommending virtual objects, comprising:
responding to a trigger instruction for displaying a display interface containing a virtual object, and acquiring first image information of a target media object, wherein the target media object is determined according to user characteristic data;
determining a degree of correlation of the first image information and a set of second image information of at least one virtual object;
determining a target virtual object recommended for the user according to the correlation;
recommending the target virtual object to the user.
2. The method for recommending a virtual object according to claim 1, wherein the media object includes a song, the first image information includes a song cover and a song cover, the second image information includes a virtual object cover, and the correlation includes a histogram chi-squared coefficient and a histogram intersection coefficient;
the determining a degree of correlation of the first image information and a set of second image information of at least one virtual object comprises:
determining a relevance of the song cover to a set of virtual object covers of the at least one virtual object;
determining a relevance of the music style cover to a set of virtual object covers of the at least one virtual object.
3. The method for recommending virtual objects according to claim 2, wherein the determining a target virtual object recommended for the user according to the relevance comprises:
and determining a virtual object corresponding to the relevancy meeting the preset relevancy rule in the at least one virtual object as a target virtual object recommended to the user.
4. The method for recommending virtual objects according to claim 2, wherein the determining a target virtual object recommended for the user according to the relevance comprises:
acquiring user characteristic data;
inputting the user characteristic data and the relevancy into a recommendation model for processing to obtain a recommendation value of the virtual object when the virtual object faces the user;
and determining the virtual object with the recommendation value meeting the recommendation condition as the target virtual object.
5. The method for recommending a virtual object according to claim 4, wherein the recommendation model is obtained by:
acquiring a training data set, wherein the training data set comprises a plurality of training samples, and the training samples comprise virtual object covers, song style covers and user characteristic data;
for a training sample in the training data set, determining the correlation degree of a song cover and a virtual object cover in the training sample and the correlation degree of the song cover and the virtual object cover; and training a recommendation model according to the similarity corresponding to the training sample and the user characteristic data to obtain the trained recommendation model.
6. The recommendation method for a virtual object according to claim 4 or 5, further comprising:
and in response to the fact that historical interaction data facing the target virtual object exist in the set duration, updating the recommendation model according to the virtual object cover of the target virtual object and the first image information to obtain an updated recommendation model.
7. A computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, implement a method of recommendation for a virtual object as claimed in any one of claims 1 to 6.
8. An apparatus for recommending a virtual object, comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for responding to a trigger instruction for displaying a display interface containing a virtual object, and acquiring first image information of a target media object, and the target media object is determined according to user characteristic data;
a first determining module for determining a degree of correlation of the first image information and a set of second image information of at least one virtual object;
the second determination module is used for determining a target virtual object recommended for the user according to the correlation;
and the recommending module is used for recommending the target virtual object to the user.
9. The apparatus for recommending virtual objects according to claim 8, wherein the media object includes a song, the first image information includes a song cover and a song cover, the second image information includes a virtual object cover, and the correlation includes a histogram chi-squared coefficient and a histogram intersection coefficient;
the first determining module is specifically configured to:
determining a relevance of the song cover to a set of virtual object covers of the at least one virtual object;
determining a relevance of the music style cover to a set of virtual object covers of the at least one virtual object.
10. A computing device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the memory-stored computer-executable instructions cause the at least one processor to perform the method for recommending virtual objects according to any of claims 1 to 6.
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