Research And Application Of Recommendation Algorithm In Video Website

With the development of science and technology,people are surrounded by a variety of data and information has been overloaded.At the same time,the pace of people's life is getting faster and faster,and the time for entertainment and relaxation is getting shorter and shorter.How to mine interesting and valuable information from massive information for user,especially when user has no objective.Personalized recommendation system has successfully solved this problem.At present,there are various recommendation systems,such as video recommendation system,e-book recommendation system and so on.However,the long tail effect has seriously reduces the resource utilization of the recommender system and hinder the enterprise to seek long-term profits.This paper aims to reduce the negative impact of the long tail effect and is devoted to the following work:Firstly,study and analysis the phenomenon of long tail effect through reading a lot of domestic and foreign literature of science and technology.Find out the current solution to solve this problem and compare the advantages and disadvantages of these solutions.Secondly,to address long tail effect in video sites,this paper advances an improved user-based collaborative filtering algorithm which called PGSim-CF.This approach presented in the paper calculates the initialized weight of item based on the PageRank algorithm,and then optimizes the weight of cold item through the weight of popular item in the same category.Finally,the result of the experiment on the MovieLens database show that the proposed algorithm reduce the impact of the long tail effect and have a better performance than traditional collaborative filtering recommendation algorithm,SimCF-AC algorithm and IRM2 algorithm.Thirdly,this subject apply PGSim-CF algorithm to the Internet video website and the operator video on demand system.This subject has completed the architecture design of recommender system,underlying storage,data cleaning,recommendation engine design which include real-time engine and offline engine,adjusting model parameters.