CN111031362B - Age prediction method for voice live broadcast user - Google Patents
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
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- H—ELECTRICITY
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/251—Learning process for intelligent management, e.g. learning user preferences for recommending movies
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
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- H04N21/25891—Management of end-user data being end-user preferences
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
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- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
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- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
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Abstract
The invention discloses an age prediction method for a voice live broadcast user, which comprises the following steps: constructing an image system according to the information of the live broadcast users with known ages and voices; constructing an image system according to the information of the voice live broadcast user with unknown age; training a user age generation model according to an image system of a live broadcast user with voice of a known age and an image system of a live broadcast user with voice of an unknown age, and generating the age of the live broadcast user with voice of the unknown age. The age of a live user with an unknown age sound can be predicted.
Description
Technical Field
The invention relates to an age prediction method for a voice live broadcast user.
Background
To obtain the age group preferences of users, an age prediction system is first provided to support the matching and prediction of the age group for each user or anchor. The current user age field is simply grouped according to the user-defined age, and then is filled according to the average value in the group when the missing value is encountered, and obviously, the method has the following defects: firstly, when the method is further used for recommendation, if the preference is not subdivided under the condition of depending on the age preference, the age preference of a positioning user is very coarse granularity, because the method depends on the age information filled by the user, certain inaccuracy exists in data, and for the data with missing age, the data is only filled according to the average value in a group, so that the method is not a good solution. Therefore, an intelligent and subdivision mode is needed to solve the problem of classification and prediction of the age group of the user, so as to serve accurate personalized recommendation.
Disclosure of Invention
The invention aims to solve at least one of the problems in the prior related art to a certain extent, and therefore the invention aims to provide an age prediction method for a voice live broadcast user, which is automatic and intelligent and can automatically complete the age prediction of the user in a model mode.
The above purpose is realized by the following technical scheme:
a method for predicting the age of a voice live broadcast user comprises the following steps: constructing an image system according to the information of the live broadcast users with known ages and voices; constructing an image system according to the information of the voice live broadcast user with unknown age; training a user age generation model according to an image system of a live broadcast user with voice of a known age and an image system of a live broadcast user with voice of an unknown age, and generating the age of the live broadcast user with voice of the unknown age.
As a further improvement of the invention, the establishment of the system for representing the live users with the known age and the live users with the unknown age comprises the following steps: the automatic collection of the related information is completed through the system information; constructing supplementary information through data mining and data statistics; and based on the related information and the supplementary information, merging and clustering are carried out, and a voice live broadcast user portrait system is constructed.
As a further improvement of the invention, the related information comprises a user wave band number, a user id, a user equipment id, a user nickname, a user occupation, a user frequent place, the latest live broadcast listening time of the user, the first live broadcast listening time of the user and age group preference of the user.
As a further improvement of the present invention, the supplementary information of the user includes an effective program listening rate of the user, an broadcasting completion rate of the user, a payment rate of the user, a main broadcasting attention number of the user, a primary tag preference of the user for listening to the live sound, a secondary tag preference of the user for listening to the live sound, a main broadcasting gender preference of the user for listening to the live sound, and a time distribution of opening an app by the user.
As a further improvement of the present invention, the related information further includes an app installation list of the live voice user.
As a further improvement of the invention, the related information acquired by the voice live broadcast user with known age also comprises the age of the user.
As a further improvement of the method, historical behavior information of the voice live broadcast user, preference information of the voice live broadcast user to the anchor, activity information of the voice live broadcast user and app installation list information of the voice live broadcast are obtained by merging and clustering the related information and the supplementary information.
As a further improvement of the invention, the model is trained according to the following steps: the method comprises the following steps: extracting a plurality of real name authenticated users from the sound live broadcast app as training data; step two: generating a user age characteristic vector according to the behavior of a user in voice live broadcast; step three: model prediction; step four: and (6) optimizing the model.
As a further improvement of the present invention, information obtained by merging and clustering the related information and the supplementary information is processed to obtain regression features, and the regression features include: generating behavior characteristics according to historical behavior information of a voice live broadcast user; performing tfidf and svd dimension reduction according to the preference information of the sound live broadcast user to the anchor; generating user active sequence data embedding according to activity information of a voice live broadcast user; supplementing the category information of the apps through knn according to the app installation list of the sound live broadcast user, and performing tfidf and svd dimension reduction.
As a further improvement of the present invention, the regression features were trained using lightGBM and mlp, respectively, for the age prediction model, followed by stacking.
Compared with the prior art, the invention at least comprises the following beneficial effects:
1. the invention provides an age prediction method for a voice live broadcast user, which is automatic and intelligent and can automatically complete the age prediction of the user in a model mode.
2. During prediction, historical behavior information of the user in the sound live broadcast app, preference information of the user to the anchor, activity information, app installation list information of the sound live broadcast user and the like are integrated, and the characteristics of the user can be depicted more objectively and in more dimensions.
Drawings
FIG. 1 is a flow chart of a method for predicting the age of a live voice user;
FIG. 2 is a schematic flow chart of a method for predicting the age of a live voice user;
fig. 3 is a schematic diagram of another process of the method for predicting the age of a live voice user.
Detailed Description
The present invention is illustrated by the following examples, but the present invention is not limited to these examples. Modifications to the embodiments of the invention or equivalent substitutions of parts of technical features without departing from the spirit of the invention are intended to be covered by the scope of the claims of the invention.
Referring to fig. 1-3, the method for predicting the age of a live voice user according to the present invention comprises the following steps: constructing an image system according to the information of the live users with known ages by voice S1; constructing an image system according to the information of the live users with unknown age and voice S2; training a user age generation model based on the representation system of the live broadcast user with known age voice and the representation system of the live broadcast user with unknown age voice, and generating the age of the live broadcast user with unknown age voice S3.
The method for constructing the live broadcast user portrait system of the voice with the known age and the voice with the unknown age comprises the following steps: the automatic collection of the related information is completed through the system information; constructing supplementary information through data mining and data statistics; and based on the related information and the supplementary information, merging and clustering are carried out, and a voice live broadcast user portrait system is constructed.
The related information comprises a user wave band number, a user id, a user equipment id, a user nickname, a user occupation, a user frequent place, the latest live broadcast listening time of the user, the first live broadcast listening time of the user and the age group preference of the user. Other relevant information that the system can automatically collect is also included.
The supplementary information of the user comprises the effective program listening rate of the user, the broadcasting completion rate of the user, the payment rate of the user, the number of concerned anchor broadcasts of the user, the first-level tag preference of the listening sound live broadcast of the user, the second-level tag preference of the listening sound live broadcast of the user, the anchor broadcast gender preference of the listening sound live broadcast of the user and the time distribution of opening the app of the user. The construction of the supplemental information includes, but is not limited to, the information mentioned above.
Preferably, the related information further includes an app installation list of the live voice user. The collection of the information of the sound live broadcast user comprises the related information of the user, which can be obtained in the sound live broadcast app, the construction of supplementary information through data mining and data statistics, and other information outside the sound live broadcast app, and the information can be used for objectively and more dimensionally depicting the portrait system of the user.
Preferably, the related information acquired by the voice live broadcasting user with the known age further comprises the age of the user. And acquiring the age of the user, and acquiring the relationship between the acquired various information and the age of the user.
And obtaining historical behavior information of the voice live broadcast user, preference information of the voice live broadcast user to the anchor, activity information of the voice live broadcast user and app installation list information of the voice live broadcast by combining and clustering the related information and the supplementary information. The information is merged and clustered, and is sorted, so that the age can be conveniently predicted.
Preferably, the ages of the users with live voice are classified into the following categories: user category 1 at 0-12 years of age; user category 2 at age 13-20; user category 3 at age 21-30; user category 4 at age 31-40; user category 5 at age 41-50; user category 6 at age 51-60; user category 7 at age 61-70; the user is category 8 above 71. The user ages are predicted, the preferences of the users are predicted according to the user ages, information recommendation is carried out, and the preferences or the likes of the users with similar ages are similar, so that the user ages can be classified. The age bracket of the user is predicted, the actual requirement is met, and the pressure of the system for collecting information is reduced.
Training the model according to the following steps: the method comprises the following steps: extracting a plurality of real-name authenticated users from the voice live app as training data, preferably, the number distribution of the users under each age classification approaches to the same, preventing imbalance, and continuously adding manually marked data at a later stage to increase the reliability of the data; step two: generating a user age characteristic vector according to the behavior of a user in voice live broadcast; step three: model prediction; step four: and (6) optimizing the model.
And processing the information obtained after the relevant information and the supplementary information are merged and clustered to obtain regression characteristics, wherein the regression characteristics comprise: generating behavior characteristics according to historical behavior information of a voice live broadcast user; performing tfidf and svd dimension reduction according to the preference information of the sound live broadcast user to the anchor; generating user active sequence data embedding according to activity information of a voice live broadcast user; supplementing the category information of the apps through knn according to the app installation list of the sound live broadcast user, and performing tfidf and svd dimension reduction.
Tfidf is a commonly used weighting technique for information retrieval and data mining, where TF means word frequency and IDF means inverse text frequency index. SVD is singular value decomposition. embedding is a discrete data serialization method. Knn is a proximity algorithm.
The regression features were trained using lightGBM and mlp, respectively, for age prediction models, followed by stacking. Among them, lightGBM, a GBDT-based lifting method, is a fully-scaled gradient descent tree, which is one of the best algorithms for fitting the true distribution in the conventional machine learning algorithm. mlp is a multi-layer perceptron. Stacking is a hierarchical fusion model.
The above preferred embodiments should be considered as examples of the embodiments of the present application, and technical deductions, substitutions, improvements and the like similar to, similar to or based on the embodiments of the present application should be considered as the protection scope of the present patent.
Claims (4)
1. A method for predicting the age of a voice live broadcast user is characterized by comprising the following steps: the method comprises the following steps:
constructing an image system according to the information of the live broadcast users with known ages and voices;
constructing an image system according to the information of the voice live broadcast user with unknown age;
training a user age generation model according to an image system of a live broadcast user with voice of a known age and an image system of a live broadcast user with voice of an unknown age to generate the age of the live broadcast user with voice of the unknown age,
wherein, the live user portrait system of the live user of the sound of the known age and the live user of the sound of unknown age of the construction includes:
the method comprises the steps that automatic collection of related information is completed through system information, wherein the related information comprises a user wave band number, a user id, a user equipment id, a user nickname, a user occupation, a user frequent place, the latest live broadcast listening time of a user, the time when the user listens to the live broadcast for the first time, and the age group preference of the user, and the related information further comprises an app installation list of a sound live broadcast user;
constructing supplementary information through data mining and data statistics, wherein the supplementary information of the user comprises the effective program listening rate of the user, the broadcasting completion rate of the user, the payment rate of the user, the concerned anchor number of the user, the primary tag preference of the user for listening to the sound live broadcast, the secondary tag preference of the user for listening to the sound live broadcast, the anchor gender preference of the user for listening to the sound live broadcast, and the time distribution of opening the app by the user;
based on the related information and the supplementary information, merging and clustering are carried out, and historical behavior information of the sound live broadcast user, preference information of the sound live broadcast user to the anchor, activity information of the sound live broadcast user and the number of apps installed under each app classification of the sound live broadcast user are obtained by merging and clustering the related information and the supplementary information, so that a sound live broadcast user portrait system is constructed;
and processing the information obtained after the relevant information and the supplementary information are merged and clustered to obtain regression characteristics, wherein the regression characteristics comprise:
generating behavior characteristics according to historical behavior information of a voice live broadcast user;
performing tfidf and svd dimension reduction according to the preference information of the sound live broadcast user to the anchor;
generating user active sequence data embedding according to activity information of a voice live broadcast user;
supplementing the category information of the missing app by knn according to the app installation list of the sound live broadcast user, and performing tfidf and svd dimension reduction.
2. The method of claim 1, wherein the method comprises: the related information acquired by the live broadcast user with the known age also comprises the age of the user.
3. The method of claim 1, wherein the method comprises: training the model according to the following steps: the method comprises the following steps: extracting a plurality of real name authenticated users from the sound live broadcast app as training data; step two: generating a user age characteristic vector according to the behavior of a user in voice live broadcast; step three: model prediction; step four: and (6) optimizing the model.
4. The method of claim 1, wherein the method comprises: the regression features were trained using lightGBM and mlp, respectively, for age prediction models, followed by stacking.
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| CN104700843A (en) * | 2015-02-05 | 2015-06-10 | 海信集团有限公司 | Method and device for identifying ages |
| CN106651057A (en) * | 2017-01-03 | 2017-05-10 | 有米科技股份有限公司 | Mobile terminal user age prediction method based on installation package sequence table |
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