关键词: 标签, 个性化推荐, 信任关系, 时间信息

Abstract: Most of the label-based recommendation algorithms have a problem that the recommend approach is singleness, and do not make full use of other information such as social relations. Aiming at this problem, on the basis of existing algorithms, a matrix factorization personalized recommendation algorithm fusing label popularity, time weight and trust relationship（TTLMF） is proposed. TTLMF on the basis of the existing label-based personalized recommendation algorithm, makes full use of the trust relationship between users and the current context of the time information, which makes the recommended projects more in line with the needs of users. Experimental results in the dataset of Last.fm show that the TTLMF algorithm has a better recommendation effect on four evaluation metrics which are precision, recall, F-measure and coverage, and also alleviates the sparseness of data and the cold start problem of users to a certain degree.

Key words: label, personalized recommendation, trust relationship, time information