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中文摘要：
针对传统协同过滤推荐算法在计算用户相似度时只考虑单一的用户评分矩阵问题,提出一种结合时间权重与信任关系的协同过滤推荐算法TTCF。首先通过标签的流行度刻画用户对资源的偏好,并利用用户的时间行为信息获得用户兴趣相似度;然后通过一级与二级好友扩展用户熟悉相似度,并与兴趣相似度加权获得最终的用户相似度;最后结合用户相似度和时间衰减项为用户产生推荐。在数据集Last.fm上的实验结果表明该算法具有较好的推荐效果。
英文摘要：
For the reason that traditional collaborative filtering algorithms only consider the single user rating matrix in calculating users＇ similarity, this paper proposed a collaborative filtering recommendation algorithm combining time weight and trust relationship TTCF, which first used tags＇ popularity to portray users＇ preferences for resources, and used users＇ time behavior information to obtain users＇ interest similarity, then considered the trust relationship between users, and used the primary and secondary friends to extend users＇ familiarity similarity and then got the end users＇ similarity with interest similarity and familiarity similarity, and at last, combining with users＇ similarity and time information to generate recommendations for users. The experimental results on the dataset of Last. fm show that the algorithm has a better recommendation results.
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