计算机工程与应用2019，Vol.55Issue(5)：159-165,7.DOI:10.3778/j.issn.1002-8331.1711-0330
融合标签和多元信息的个性化推荐算法研究
Research on Personalized Recommendation Algorithm Based on Label and Multi-Information
摘要
Abstract
Most of the label-based recommendation algorithms have a problem that the recommend approach is single-ness, 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 algo-rithm, 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.关键词
标签/个性化推荐/信任关系/时间信息Key words
label/ personalized recommendation/ trust relationship/ time information分类
信息技术与安全科学引用本文复制引用
张鹏飞,王宜贵,张志军..融合标签和多元信息的个性化推荐算法研究[J].计算机工程与应用,2019,55(5):159-165,7.基金项目
国家自然科学基金(No.71471106). （No.71471106）
