期刊
INFORMATION SCIENCES
卷 180, 期 24, 页码 4929-4939出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.08.044
关键词
News recommendation; Recommender system; Content-based filtering; Collaborative filtering; Social media; User comment; Information retrieval
资金
- Scientific Research Starting Foundation for Returned Overseas Chinese Scholars
- National Natural Science Foundation of China (NSFC) [60803106]
- Fok Ying-Tong Education Foundation, China [121068]
- Natural Sciences and Engineering Research Council (NSERC) of Canada [303958-2010]
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.
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