Video-on-demand program recommendation method based on user set

Abstract

The invention discloses a video-on-demand program recommendation method based on a user set. The method comprises the following steps of: 1) collecting the watching records of mass users, setting basic user types, matching the basic user types with film classification labels, and classifying a label weight table for different basic user types; 2) collecting the watching records of the users, carrying out calculation to obtain the model FUser (Li) of a user to be recommended, determining the type of the user to be recommended, and taking a corresponding film classification label weight table as a filtering rule; and 3) collecting the watching records of the users, using an ALS (Alternating Least Squares) algorithm to generate a film recommendation list, and generating a final film recommendation list through the filtering rule. By use of the method, the accuracy and the coverage rate of video recommendation oriented to television end users are high and are unlikely to be affected by popular videos, and a phenomenon that video recommendation information received by the user is repeated is avoided. By use of the method, on the basis of the ALS recommendation algorithm, the characteristics of the television end users are combined to optimize a recommendation result and improve the watching experience of the television end users.

Description

technical field [0001] The invention relates to the technical field of Internet video aggregation, in particular to a method for recommending video-on-demand programs based on user sets. Background technique [0002] With the development of Internet technology, how to push massive videos to users has become an urgent problem to be solved. Most of the existing Internet video recommendation algorithms on the TV side are based on buyer-based collaborative filtering or product-based collaborative filtering proposed by traditional e-commerce websites. filter. These two methods are relatively simple and easy to implement, but the recommendation accuracy and coverage are not very ideal. [0003] At present, most of the recommendation algorithms in the video field are derived from the buyer-based collaborative filtering method (user-based collaborative filtering method). The similarity between two videos), and then according to the viewing records of a single user, select several ...

AI Technical Summary

Problems solved by technology

The reason is that this method fuzzily infers user characteristics through user behavior, and does not use real user characteristics, especially TV end user characteristics.

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