User comments for news recommendation in forum-based social media
Information SciencesPublished 4 September 2010
Qing Li, Jia Wang, Yuanzhu Peter Chen, Zhangxi Lin
Citations137
SJR quartileQ1
SJR score1.80
SNIP1.98
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TL;DR
The relationship (duplicate, generalization, or specialization) between recommended news articles and the original posting is investigated and the proposed solutions provide an improved news recommendation service in forum-based social media.
Abstract
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 "push-data" 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.
Keywords
Computer Science
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