The Integrated Similarity Based Project Collaborative Filtering Algorithm

Internet has the world’s largest and most complete information resource.Abundant resources bring the convenience to people’s lives and learning.Theresources also lead to an information overload problem. How to make no clear targetfor users who can accurately find the information resources become a problemwhich information producers are facing.The system of Recommendation can solvethis problem.The key is algorithm of the recommendation.This paper should build personalized movie recommendation system.Wecommonly used content-based recommendation algorithm which is not suitable formovie information.There was no relationship between friends movie system.Socialnetworks are not suitable for movie recommendation system recommended so thatthe system uses collaborative filtering recommendation. Therefore the project-basedcollaborative filtering algorithm is clearly more suitable for user-based collaborativefiltering method. The traditional project-based collaborative filtering algorithmsconsider only user rating matrix.It is recommended that the quality is not high. Inthis paper, the similarity calculation improved project-based collaborative filteringalgorithms.The main work is as follows:1) Proposing a comprehensive similarity is the similarity of projects and thesimilarity between categories.Traditional integrated similarity calculation.Projectand project similarity between the coefficients and item category similarity betweenthe coefficients and is one can not be combined well explained reasons. MAE is themean absolute error of judging the merits of a standard recommendation algorithm.The experiment verifies the traditional algorithm and improved algorithm merits.2) The algorithm of Recommendation is complexity. Naturally spend more timein the use of project-based Hadoop technology can be integrated on the similaritycomputation and parallel Top-N recommendation. By the time that the increase rateof the parallel computing complexity reducing the time and improve the userexperience results.