An Optimized Tag Recommender Algorithm in Folksonomy

摘要：

　　In the existing folksonomy system, users can be allowed to add any social tags to the resources, but tags are fuzzy and redundancy in semantic, which make it hard to obtain the required information for users.An optimized tag recommender algorithm is proposed to solve the problem in this paper.First, based on the motivation theory, the recommender system uses the model given to calculate the user retrieval motivation before searching information.Second, we use the results in first step to distinguish the users type and then cluster the resources tagged according to users who have the similar retrieval motivation with k-means++ algorithm and recommend the most relevant resources to users.The experimental results show that our proposed algorithm with user retrieval motivation can have higher accuracy and stability than traditional retrieval algorithms in folksonomy system.

关键词： Folksonomy tag recommender system collaborative filtering user retrieval motivation k-means++

作者: Jie Chen Baohua Qiang Yaoguang Wang Peng Wang Jun Huang

作者单位: Guilin University of Electronic Technology, Guilin 541004, China Guilin University of Electronic Technology, Guilin 541004, China;Guangxi Key Laboratory of Trusted S

会议类型: 国际会议

会议名称: 8th International Conference on Intelligent Information Processing(2014年IFIP智能信息处理国际会议)

会议地点: 杭州

会议语种:英文

页码: 47-56

在线出版日期: 2014-10-01（万方平台首次上网日期，不代表论文的发表时间）