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Question Recommendation for Collaborative Question Answering Systems with RankSLDA

Collaborative question answering (CQA) communities rely on user participation for their success. This paper presents a supervised Bayesian approach to model expertise in on-line CQA communities with application to question recommendation, aimed at reducing waiting times for responses and avoiding question starvation. We propose a novel algorithm called RankSLDA which extends the supervised Latent Dirichlet Allocation model by considering a learning-to-rank paradigm. This allows us to exploit the inherent collaborative effects that are present in CQA communities where users tend to answer questions in their topics of expertise. Users can thus be modeled on the basis of the topics in which they demonstrate expertise. In the supervised stage of the method we model the pairwise order of expertise of users on a given question. We compare RankSLDA
against several alternative methods on data from the Cross Validate community, part of the Stack Exchange network. RankSLDA outperforms all alternative methods by a signiffcant margin