Research On Film Recommendation Algorithm Based On Fusion Model

With the development of society and the progress of science and technology,the proportion of people interacting with the Internet in daily life is increasing.Watching videos,MVs,listening songs,and other entertainment programs through Internet sites or mobile electronic devices are becoming common ways for the general public to spend their leisure time.At the same time,in order to cater to popular taster,various kinds of films,videos,and other items that are presented in front of the audience in various major Internet platforms.Gradually,people have changed from the lack of information and entertainment to the explosion of information.This state is what we call information overload.In the current era of information overload,how do consumers find what they really need in the flood of other non-essential information,without being disturbed and misled by other information.how do producer show up prominently and be valued by the general public,are now challenges for us.The recommendation system is to give an effective solution to the above problem of information overload.The basic task of the recommendation system is to connect items and users,solve the problem of information overload,and use different recommendation algorithms to associate users with items in a specific way so as to target different users and provide personalized services according to their interests recommend.In order to improve the accuracy and diversity of the advancing algorithm and achieve better recommendation results,this paper improve the different algorithms and obtained a new weighted fusion model.The main work of this dissertation is as follows:A fusion recommendation model for film recommendation is proposed,and the improvement of the algorithm is proposed on the sub-algorithm that forms the fusion model,so as to improve the diversity and accuracy of the recommendation algorithm of the film product.(1)Based on the user-based collaborative filtering algorithm of the recommendation system,the time context information for viewing the movie by the user is added and analyzed,so that the neighbor user with higher similarity with the predicted user is more accurately obtained,thereby improving the accuracy of the algorithm.(2)based on the item-based collaborative filtering of articles,the accuracy of the algorithm is improved by penalizing the improper contribution of active users at the same time.(3)Improve the graph-based collaborative filtering algorithm,and reconstruct a graph-based collaborative filtering algorithm by changing different weights,so as to improve the accuracy of the algorithm.The off-line experiment was used to calculate the accuracy of various algorithms before and after improvement,and a fusion recommendation model based on the above algorithm was proposed.Off-line experiments proved that the fusion recommendation model had better improvements in accuracy and diversity.