A Joint Webpage Recommendation Framework with Content Features, Associate Features and Static Features

摘要：

Recommendation gains great succuss in many applications, such as e-commerce, music sharing and news releasing. In the other hand, web is becoming the main channel to release and obtain information, therefore it is meaningful to design a webpage recommendation system to help conveniently navigate on the internet. Existing recommendation approaches often focus on a single feature, which cant satisfy webpage recommendation requirement completely due to the missing of its inherent rich semantics. In this paper, we present a hybrid webpage recommendation framework combining different features together by a linear model, including content features, associate features and static features. Experimental results show that our approach outperforms the approach with single feature and it can indeed provide promising recommendation service.

关键词： webpage recommendation content feature associate feature static feature

作者: Linkai Weng Yaoxue Zhang Yuezhi Zhou Yu Bai

作者单位: Department of Computer Science & Technology University of Tsinghua

会议类型: 国际会议

会议名称: 2011 International Conference on Database and Data Mining(ICDDM 2011)(2011年数据库和数据挖掘国际会议)

会议地点: 三亚

会议语种:英文

页码: 88-92

在线出版日期: 2011-03-25（万方平台首次上网日期，不代表论文的发表时间）