Broadcasting and television program recommendation system and method

Abstract

The invention provides a broadcasting and television program recommendation system and method. The method includes the steps: inputting an object to be recommended, an audience preference index and an audience time period; storing program playing information as a program data source; storing audience information as an audience data source; calling the program data source and the audience data source and calculating the audience preference index of each user to form an audience preference matrix; calculating a connection relation value between the users according to the audience preference matrix, taking each user as a node and building a path between two users to form a connection network when the connection relation value between the users is larger than a set threshold value; determining credibility between the users according to the node and the path; determining the recommendation level of different users for different programs according to credibility and audience preference; selecting a set number of users according to the sequence of credibility from high to low, selecting a set number of programs according to the sequence of the recommendation level of the set number of users from high to low, and generating a program list of each user.

Description

technical field [0001] The present invention relates to the field of radio and television, and more specifically, to a system and method for recommending radio and television programs. Background technique [0002] The great abundance of radio and TV program resources has brought about the diversification of program type preferences of viewing users. Segmenting users, finding user groups with different viewing preferences, and recommending personalized programs are urgent problems in the field of radio and television. In the field of traditional broadcasting and television, the random telephone survey method or the diary recording method are used to study users' viewing behavior, and it is impossible to establish the collected sample data of users on the entire network, and the research on program recommendation technology is progressing slowly. With the advent of the digital era of broadcasting and television, the establishment of the user rating data and program broadcasti...

AI Technical Summary

Problems solved by technology

[0003] Existing personalized recommendation methods mainly include collaborative filtering recommendation and content-based recommendation. These two methods focus on how to obtain user's historical behavior information, how to represent the feature information of the target item, and how to associate user interests with item feature information. Corresponding, but due to data sparsity and cold start problems, the recommendation effect has great limitations. For example, the user information with different interests and preferences is not considered to interfere with the recommendation effect, and the influence of network topology on the recommendation effect is not considered.

Images