Collaborative Filtering Recommendation Model Considering Integration of User Rating and Attribute Similarity

Abstract

Directed at the problem that the collaborative filtering algorithm tends to be subject to data sparsity and cold boot, a collaborative filtering recommendation algorithm based on improvement nearest neighbor is proposed. Firstly, the current user’s k nearest neighbor and reverse k nearest neighbor are obtained on the basis of the similarity algorithm, which are used to compute positive and negative credibility values respectively based on their predicted ratings and the current user ratings. Then modifications of constraint are made for the users who are both the k nearest neighbor and the reverse k nearest neighbor and the hot resources. Finally, the collaborative filtering recommendation algorithm based on weight fusion is derived and a comparative experiment of simulation is conducted on MovieLens. The result shows that the algorithm in the Thesis decreases the mean absolute error value while improving the accuracy of recommendation.