A personalized recommendation service method for Internet TV for large-scale users

A network TV and recommendation service technology, which is applied in the field of network TV personalized recommendation service for large-scale users, can solve the problems of low recommendation accuracy, low real-time performance, and poor scalability, and achieve the solution of the problem of low real-time recommendation Strong, improve the effect of personalized recommendation accuracy

Active Publication Date: 2020-05-05
SHANDONG UNIV OF SCI & TECH
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  • Claims
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AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems of information overload, low recommendation accuracy, poor real-time performance, and poor scalability when existing Internet TV is oriented to large-scale users and massive resources, the present invention provides a large-scale user-oriented Internet TV Personalized Recommendation Service Method

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  • A personalized recommendation service method for Internet TV for large-scale users

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Embodiment Construction

[0027] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing and specific embodiment:

[0028] combine figure 1 , a method for personalized network TV recommendation service for large-scale users, comprising the following steps:

[0029] Step 1: Internet TV user portrait modeling:

[0030] (1) Complete the collection and processing of information from large-scale Internet TV users, and collect users' basic attribute data, behavior tendency data and content preference data;

[0031] (2) Preprocess the collected data. After the data processing is completed, predict the user's unknown attributes and behaviors through data mining, text mining, and natural language processing technologies;

[0032] (3) Use the user's basic attribute information to classify through the Bayesian classification algorithm to complete the user's static attribute modeling, and use the user's behavior tendency and content preference beh...

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Abstract

The present invention provides a large-scale user-oriented network TV personalized recommendation service method, using data such as large-scale users and user attributes and behaviors, to realize user portrait modeling based on big data, and by extracting time, location, weather, etc. Scenario information, build context modeling based on big data. In the offline computing stage, solve the problem of algorithm scalability through double clustering of users and items, and combine matrix decomposition, collaborative filtering and other technologies to train models and fitting parameters; in the online computing stage, design incremental computing recommendations based on quadratic matrix sampling The model uses newly added interactive data to realize real-time online recommendation. In the end, the recommendation results of the offline and online models are combined to form an initial Top-K recommendation list, and then the information in the context-based modeling is used to complete the context filtering to form the final Top-N recommendation list, which improves the serviceability for large-scale users. Network TV personalized recommendation accuracy.

Description

technical field [0001] The invention relates to the field of network TV personalized recommendation service, in particular to a large-scale user-oriented network TV personalized recommendation service method. Background technique [0002] Internet TV has changed the traditional and passive viewing mode of users, and made it possible to independently broadcast a large number of video resources on the Internet. However, the types and quantities of resources are growing faster and faster. Obtaining valuable information has become an important bottleneck restricting the development of Internet TV, and personalized recommendation technology can effectively solve this problem. [0003] Since 1990, recommender system technology has developed vigorously, and various practical recommender system schemes have been proposed in the industry. The recommendation system has evolved from the initial e-commerce recommendation to music, movies, social networking, reading, O2O, advertising, t...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06K9/62
CPCG06F18/24155
Inventor 傅正斌赵建立耿夕娇肖玉王伟
Owner SHANDONG UNIV OF SCI & TECH
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