Abstract
Web 2.0 applications attract more and more people to express their opinions on the Web in various ways. However, the explosively increasing information in social web sites requires an effective mechanism to timely filter and summarize social common interest, and the moderator needs this mechanism as well to recommend the proper posts and guide public discussions. In this paper, we discuss the problem of recommending post in online communities: we firstly cluster the posts in groups based on their semantic relations, then filter the potential clusters by computing the cluster’s support, and finally select the recommended posts as content representatives considering global and local support from each clusters. We compare different feature selections between tags, keywords and topics on cluster formation, and discuss their differences. The human judgement in our experiment shows that the recommendation based on marked tags is much more effective and concise than those on keywords and hidden topics.
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© 2009 Springer-Verlag Berlin Heidelberg
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Wang, L., Bross, J., Meinel, C. (2009). Post Recommendation in Social Web Site. In: Wimmer, M.A., Scholl, H.J., Janssen, M., Traunmüller, R. (eds) Electronic Government. EGOV 2009. Lecture Notes in Computer Science, vol 5693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03516-6_18
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DOI: https://doi.org/10.1007/978-3-642-03516-6_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03515-9
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