融合用户兴趣和评分差异的协同过滤推荐算法

Collaboration Filtering Recommendation Algorithm Based on User Interest and Ratings Difference

针对传统的协同过滤算法中单一评分相似性计算不准确的问题,提出融合用户兴趣和评分差异的协同过滤推荐算法。将TF-IDF思想运用到用户对标签的权重计算中,并使用指数衰减函数和时间窗口捕捉用户兴趣的变化;根据历史评分矩阵,充分考虑用户评分值差异、评判准则差异、影响力差异和项目影响差异等影响因子,定义了一种评分差异相似性度量算法;最后将用户兴趣相似性和评分差异相似性进行加权融合,获取更加准确的用户邻居,从而预测项目评分并进行推荐。在数据集Movielens的实验表明,提出的算法能有效提高推荐精度。

Aiming at the inaccuracy of single rating similarity calculation in traditional collaborative filtering algorithm, a collaborative filtering recommendation algorithm based on user interest and the ratings difference is proposed. Firstly, the TF-IDF is integrated into the tag weight calculation. An exponential decay function and a time window are used to capture the change of user interest. Secondly, according to the rating matrix, ratings difference similarity algorithm is defined by considering the difference of ratings, evaluation criteria, user and item influence. Finally, user interest similarity and ratings difference similarity are weighted and fused to obtain the nearest neighbors more accurately, which predicts item rating and makes recommendations. Experiments on the Movielens dataset show that the proposed algorithm can effectively improve the recommendation accuracy.