Personalized recommendation of stories for commenting in forum-based social media

详细信息    查看全文

• 作者：Ngo Xuan Bach ; ^a ; ^c ; ^{ngoxuanbachkcn@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; ^{bachnx@ptit.edu.vn" class="auth_mail" title="E-mail the corresponding author} ; Nguyen Do Hai^b ; ^{haind2@fsoft.com.vn" class="auth_mail" title="E-mail the corresponding author} ; Tu Minh Phuong^a ; ^c ; ^{phuongtm@ptit.edu.vn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Internet forums ; Online news services ; Content-based filtering ; Collaborative filtering ; Hybrid recommendation ; Learning-to-rank
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：20 July 2016
• 年：2016
• 卷：352-353
• 期：Complete
• 页码：48-60
• 全文大小：1261 K

文摘

Web 2.0 platforms such as blogs, online news, social networks, and Internet forums allow users to write comments to express their interests and opinions about the content of news articles, videos, blogs or forum posts, etc. Users’ comments contain additional information about the content of Web documents as well as provide important means for user interactions. In this paper, we present a study on the task of recommending, for a given user, a short list of suitable stories for commenting. We propose an efficient collaborative filtering method which exploits co-commenting patterns of users to generate recommendations. To further improve the accuracy, we also introduce a novel hybrid recommendation method that combines the proposed collaborative features and content based features in a learning-to-rank framework. We verify the effectiveness of the proposed methods on two datasets including samples of user comments from an online forum and a forum-based news service. Experimental results show that the proposed collaborative filtering method substantially outperforms traditional content-based approaches in terms of accuracy. Furthermore, the proposed hybrid approach leads to additional improvements over individual recommendation methods and achieves higher accuracy than a baseline hybrid approach. The results also demonstrate the stability of our methods in handling newly posted stories with a small number of comments.