Tag-based Smoothing for Item Recommendation

摘要：

Collaborative tagging sites allow users to save and annotate their favorite Web contents with tags. These tags provide a novel source of information for collaborative filtering. However, major gap exists in how to integrate tagging information into traditional recommender systems for better recommendation quality, due to the difficulty to quantize these semantic tags. This paper proposes a novel approach to convert this semantic information into quantitative values from a smoothing point of view, taking advantage of the topic-based method, and then make recommendation in a traditional user-based CF fashion based on the smoothed user-item matrix. Experiments on two real-world collaborative tagging datasets prove the effectiveness of our approach.

关键词： collaborative filtering tag smoothing weighing recommender systems

作者: Jing Peng Daniel Zeng

作者单位: Institute of Automation, Chinese Academy of　Sciences, Beijing 100190, China. Institute of Automation, Chinese Academy of　Sciences and Department of Management Information System

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Service Operations and Logistics,and Informations(2010 IEEE 服务、运筹、物流与信息化国际研讨会)

会议地点: 青岛

会议语种:英文

页码: 452-457

在线出版日期: 2010-07-15（万方平台首次上网日期，不代表论文的发表时间）