User Participation Prediction in Online Forums

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 367–376, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics User Participation Prediction in Online Forums Zhonghua Qu and Yang Liu The University of Texas at Dallas {qzh,yangl@hlt.utdallas.edu} Abstract Online community is an important source for latest news and information. Accurate prediction of a user’s interest can help pro- vide better user experience. In this paper, we develop a recommendation system for online forums. There are a lot of differ- ences between online forums and formal me- dia. For example, content generated by users in online forums contains more noise com- pared 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 recommenda- tion systems, but some also provide oppor- tunities for better recommendation perfor- mance. In our recommendation system, we propose to (a) use latent topics to interpo- late with content-based recommendation; (b) model latent user groups to utilize informa- tion from other users. We have collected three types of forum data sets. Our experi- mental results demonstrate that our proposed hybrid approach works well in all three types of forums. 1 Introduction Internet is an important source of information. It has become a habit of many people to go to the in- ternet for latest news and updates. However, not all articles are equally interesting for different users. In order to intelligently predict interesting articles for individual users, personalized news recommen- dation systems have been developed. There are in general two types of approaches upon which rec- ommendation systems are built. Content based rec- ommendation systems use the textual information of news articles and user generated content to rank items. Collaborative filtering, on the other hand, uses co-occurrence information from a collection of users for recommendation. During the past few years, online community has become a large part of internet. More often, latest information and knowledge appear at on- line community earlier than other formal media. This makes it a favorable place for people seeking timely update and latest information. Online com- munity sites appear in many forms, for example, online forums, blogs, and social networking web- sites. Here we focus our study on online forums. It is very helpful to build an automatic system to sug- gest latest information a user would be interested in. However, unlike formal news media, user gen- erated content in forums is usually less organized and not well formed. This presents a great chal- lenge to many existing news article recommenda- tion systems. In addition, what makes online fo- rums different from other media is that users of online communities are not only the information consumers but also active providers as participants. Therefore in this study we develop a recommen- dation system to account for these characteristics of forums. We propose several improvements over previous work: • Latent topic interpolation: This is to address …

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