Coupled Matrix Factorization for Question Similarity

Community question answering (CQA) has provided an increasingly popular service where users ask and answer questions and access historical questionanswer pairs. As a fundamental task in CQA, question similarity measure is to compute the similarity between the queried question and the historical questions which have been solved by other users. We mine and use the most important semantic features as the semantic representation of questions, and try to incorporate the couplings of semantic features into vector space model. We propose Coupled question similarity (CQS) model, and compute the similarity in matrix factorization framework. Experiments conducted on real CQA data sets demonstrate that with the incorporation of such couplings, the performance of sentence similarity is improved compared to a variety of baseline methods significantly.

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