Collaborative filtering algorithm based on rating difference and user interest
Haihui Huang, Yu Guo, Xin Wang · 2018
Collaborative filtering algorithm is one of widely used approaches in daily life, so how to improve the quality and efficiency of collaborative filtering algorithm is an essential problem. Usually, some traditional algorithm focuses on the user rating, while they don't take the user rating differences and user interest into account. However, users who have little rating difference or have a similar interest may be highly similar. In this paper, a collaborative filtering algorithm based on scoring difference and user interest is proposed. Firstly, a rating difference factor is added to the traditional collaborative filtering algorithm, where the most appropriate factor can be obtained by experiments. Secondly, calculate the user's interest by combining the attributes of the items, then further calculate the similarity of personal interest between users. Finally, the user rating differences and interest similarity are weighted to get final item recommendation and score forecast. The experimental results on data set shows that the proposed algorithm decreases both Mean Absolute Error and Root Mean Squared Error, and improves the accuracy of the proposed algorithm.