基于改进相似度的协同过滤算法研究

Research on Collaborative Filtering Algorithm with Improved Similarity

协同过滤利用邻居用户的偏好对目标用户的偏好进行推荐预测,相似度计算是其关键。传统的相似度计算忽略了用户共同评分项目数与用户平均评分的影响,以至于在数据稀疏时不能很好地度量用户间的相似度。提出了两个修正因子来改进传统相似度,同时改进了协同过滤算法,将其应用于电影推荐。仿真结果表明,在电影推荐中,基于改进后相似度计算的协同过滤算法能取得比传统算法更低的MAE值,提高了电影推荐质量。

Collaborative filtering recommends and predicts the target user＇s preferences by using his neighbor user＇s preference. The calculation of similarity is the key. Traditional similarity calculation ignores the affection from the co-ra- ted item number rated by common users, and their average similarity rating. That causes poor similarity description among users in case of data sparse. In this paper, we proposed two factors to improve the traditional similarity calculation. Meanwhile, the collaborative filtering algorithm was improved with the improved similarity and it is applied to film recommendation. Simulation results show that the improved collaborative filtering algorithm based on the improved similarity can get a lower MAE value than the traditional method, which is helpful to improve the quality of movie recommendation.