User Participation Prediction in Online Forums

Online community is an important source for latest news and information. Accurate prediction of a user’s interest can help provide better user experience. In this paper, we develop a recommendation system for online forums. There are a lot of differences between online forums and formal media. For example, content generated by users in online forums contains more noise compared to formal documents. Content topics in the same forum are more focused than sources like news websites. Some of these differences present challenges to traditional word-based user profiling and recommendation systems, but some also provide opportunities for better recommendation performance. In our recommendation system, we propose to (a) use latent topics to interpolate with content-based recommendation; (b) model latent user groups to utilize information from other users. We have collected three types of forum data sets. Our experimental results demonstrate that our proposed hybrid approach works well in all three types of forums.