Indexed on: 15 Mar '16Published on: 08 Mar '16Published in: Information Sciences
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.