Answer Keyword Generation for Community Question Answering by Multi-aspect Gamma-Poisson Matrix Completion

Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:
Journal Article
IEEE Intelligent Systems, 2020, PP, (99), pp. 1-1
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IEEE Community question answering (CQA) recommends appropriate answers to existing and new questions. Such answer recommendation is challenging since CQA data is often sparse and decentralized and lacks sufficient information to generate suitable answers to existing questions. Matching answers to new questions is more challenging in modeling Q/A sparsity, generating answers to cold-start/novel questions, and integrating metadata about Q/A into models, etc. This paper addresses these issues by a novel statistical model to automatically generate answer keywords in CQA with multi-aspect Gamma-Poisson matrix completion (MAGIC). MAGIC is the first trial in CQA to model multiple aspects of Q/A sentence information in CQA by involving Q/A metadata, Q/A sparsity, and both lexical and semantic Q/A information in a hierarchical Gamma-Poisson model. MAGIC can efficiently generate answer keywords for both existing and new questions against nonnegative matrix factorization (MF), probability MF, and relevant Poisson factorization models w.r.t. recommending appropriate and informative answer keywords.
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