基于改进加权二部图和用户信任度的协同过滤推荐算法

Collaborative Filtering Recommendation System Based on Improved Bipartite Graph and User Reliability

基于复杂网络物质扩散原理的二部图理论在协同过滤推荐领域的应用受到越来越多学者的关注,现有算法计算邻居用户时主要考察用户对项目的正向评价,未充分考虑用户的负向评价.为进一步提高推荐算法的准确度,提出了改进算法,将用户正向评价和负向评价量化成二部图上的路径权重,控制用户能量的分配,并在邻居用户预测评分阶段考虑用户的信任度,推荐结果更加准确.采用MovieLens和Eachmovie数据集对改进算法以及现有算法进行对比实验分析,证明改进算法具有更低的平均绝对偏差.

The application of bipartite graph theory in collaborative filtering recommendation based on substance diffusion theory of complex networks has attracted more and more attention from scholars. Existing algorithms mainly consider the positive rating when calculating neighbor users, ignoring the negative rating of users. In order to improve the accuracy of recommendation algorithm, a collaborative filtering recommendation algorithm based on improved bipartite graph and user reliability is proposed. The algorithm quantifies both positive ratings and negative ratings into the weight of the path, which controls the user’s energy distribution, and takes users’ reliability into account when predicting the rating, therefore, the accuracy of recommendation result is significantly improved. A series of comparative experiments are carried out on MovieLens and Eachmove datasets. The experimental results show that the improved algorithm has lower mean absolute error.