User comments for news recommendation in forum-based social media

被引：98

作者：

Li, Qing

Wang, Jia

Chen, Yuanzhu Peter ^{[1
]}

Lin, Zhangxi ^{[2
]}

机构：

[1] Mem Univ Newfoundland, St John, NF A1C 5S7, Canada

[2] Texas Tech Univ, Lubbock, TX 79409 USA

来源：

INFORMATION SCIENCES | 2010年 / 180卷 / 24期

基金：

加拿大自然科学与工程研究理事会; 中国国家自然科学基金;

关键词：

News recommendation; Recommender system; Content-based filtering; Collaborative filtering; Social media; User comment; Information retrieval;

D O I：

10.1016/j.ins.2010.08.044

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

News recommendation and user interaction are important features in many Web-based news services. The former helps users identify the most relevant news for further information. The latter enables collaborated information sharing among users with their comments following news postings. This research is intended to marry these two features together for an adaptive recommender system that utilizes reader comments to refine the recommendation of news in accordance with the evolving topic. This then turns the traditional "pushdata" type of news recommendation to "discussion" moderator that can intelligently assist online forums. In addition, to alleviate the problem of recommending essentially identical articles, the relationship (duplicate, generalization, or specialization) between recommended news articles and the original posting is investigated. Our experiments indicate that our proposed solutions provide an improved news recommendation service in forum-based social media. (C) 2010 Elsevier Inc. All rights reserved.

引用

页码：4929 / 4939

页数：11

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