基于共同评分项目数和用户兴趣的协同过滤推荐方法

Collaborative filtering recommendation based on number of common items and common rating interest of users

在推荐系统中,为了在一定程度上减少用户评分数据稀疏对推荐效果的负面影响,提出了一种基于用户共同评分项目数和用户兴趣的协同过滤推荐算法。此算法将用户共同评分项目数和用户兴趣相似度相结合,使用户之间的相似度计算更加准确,为目标用户提供更好的推荐结果。仿真实验结果表明:所提算法比基于Pearson相似度计算方法的算法推荐效果更优,具有更小的平均绝对误差(MAE),表明了其有效性和可行性。

In order to reduce the negative impacts of sparse data, a new collaborative filtering recommendation algorithm was put forward based on the number of common rating items among users and the similarity of user interests. The similarity calculations were made to be more credible by combing the number of common rating items among users with the similarity of user interests, so as to provide better recommendation results for the target user. Compared with the method based on Pearson similarity, the new algorithm provides better recommendation results with smaller Mean Absolute Error（ MAE）. In conclusion,the new algorithm is effective and feasible.