A Collaborative Filtering Recommendation Algorithm Based on the Difference and the Correlation of Users’Ratings

The traditional similarity algorithm in collaborative filtering mainly pay attention to the similarity or correlation of users’ratings,lacking the consideration of difference of users’ratings.In this paper,we divide the relationship of users’ratings into differential part and correlated part,proposing a similarity measurement based on the difference and the correlation of users’ratings which performs well with non-sparse dataset.In order to solve the problem that the algorithm is not accurate in spare dataset,we improve it by prefilling the vacancy of rating matrix.Experiment results show that this algorithm improves significantly the accuracy of the recommendation after prefilling the rating matrix.