A Collaborative Filtering Recommendation Algorithm Based on Item Genre and Rating Similarity

Abstract

Aiming at the disadvantages of user-based collaborative filtering algorithm and item-based collaborative filtering algorithm on the instance of userpsilas rating datapsilas extreme sparseness, introducing the similarity of item genre and rating and improving on it. The high ratings of users group can also affect similarity when calculating the similarities of item genre and ratings. Through the experiment the improved algorithm can play down userpsilas mean absolute error and improve the quality of recommendation.