期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2015
卷号：8
期号：12
页码：205-214
DOI：10.14257/ijhit.2015.8.12.14
出版社：SERSC
摘要：Social tags providing abundant information can stimulate a better recommender system equipped with stronger sense of description and analysis on user's interest. In this paper, graph-based personalized recommendation techniques have been studied. Complete tripartite graph model was proposed and the user's interest migration was researched comprehensively, Focusing on the dilemma of accuracy and diversity in recommender system, the mass diffusion algorithm and heat spreading algorithm on complete tripartite graph model were carried out. Then, from the perspective of improving confidence in recommender system, the item-tag joint recommendation mechanism was studied. Experimental results show the effectiveness of the algorithm in this paper.
