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

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...

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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