Personalized service recommendation method of network television for large-scale users

Abstract

The invention discloses a personalized service recommendation method of network television for large-scale users. The personalized service recommendation method of network television for large-scale users utilizes large-scale user and user attribute and behavior data to realize user image modeling based on big data, and constructs context modeling based on big data by extracting scene informationsuch as time, place and weather. In the offline computing stage, the scalability problem of the algorithm is solved by user and item double clustering, and the training model and fitting parameters are combined with techniques such as matrix decomposition and collaborative filtering. In the online calculation stage, the incremental calculation recommendation model based on quadratic matrix sampling is designed, and the newly added interactive data is used to realize real-time online recommendation. Finally, the recommendation results of the offline phase and the online phase model are merged to form an initial Top-K recommendation list, and then the context filtering is performed based on the information in the context modeling to form a final Top-N recommendation list. Thus, the accuracyof personalized service recommendation of network television for large-scale users is improved.

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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