自适应用户的Item-based协同过滤推荐算法

User-adaptive Item-based collaborative filtering recommendation algorithm

传统Item-based协同过滤算法计算两个条目间相似性时,将每个评分视为同等重要,忽略了共评用户(对两个条目共同评分的用户)与目标用户间的相似性对条目间相似性的影响。针对此问题,提出了一种自适应用户的Item-based协同过滤算法。该算法将共评用户与目标用户的相似性作为共评用户评分重要性的权重,以实现针对不同的目标用户,为目标条目选择不同的、适合目标用户的最近邻居集,从而提高推荐准确性。实验结果表明,提出的算法可以显著提高推荐系统的推荐质量。

The traditional Item-based collaborative filtering algorithm regards every rating as equal importance when calculating the similarity between items, and ignores the impact of the similarity between co-rated users （ users co-rate both two items） and target user on the similarity between items. This paper proposed a user-adaptive hem-based collaborative filtering recommendation algorithm, in which the rating of a co-rated user on an item was weighted by the user similarity between the co-rated user and target user,in order to select different neighbors of a certain target item for different target users, and so as to improve the recommendation accuracy. The experiment results suggest that the proposed algorithm can efficiently improve the recommendation quality.