Music Recommendation System Based On Collaborative Filtering Algorithm

With the continuous development of network technology in recent years,information on the Internet is exploding growth of such a huge information givesusers a retrieval difficulties, traditional search engine technology is difficult to meetthe needs of users. In this context, collaborative filtering system emerged as a majorarea of electronic commerce research hotspot.Collaborative filtering is based on a group of users with similar interests orproject recommendation, which according to the user’s preference information ofneighbors of the target user generated list of recommendations. Collaborative filteringalgorithm into divided memory-based is filtering collaborative algorithms andmodel-based collaborative filtering algorithms. This paper compares the two resultson the data set and select user-based collaborative filtering algorithm, usingproject-based cosine similarity algorithm to correct our music recommendationsystem as collaborative filtering algorithm.The algorithm analyzes a certain extent make up for the deficiencies of thetraditional way of making recommendations. Meanwhile this paper, the use ofcollaborative filtering recommendation algorithm brings certain advantages to theexisting music recommendation system to do a detailed analysis, and finally using B/S software architecture to achieve a personalized music recommendation systemthroughout the software prototype.In this paper, a music recommendation prototype system user satisfactionevaluation and availability of the system were tested. Recommended by evaluatingthe results we found that the system ’s overall performance is better, to a certainextent, effectively recommended the user may be interested in the results, basicallymeeting the system design target.