Answer Keyword Generation for Community Question Answering by Multiaspect Gamma–Poisson Matrix Completion

Abstract

Community question answering (CQA) recommends appropriate answers to existing and new questions. Such answer recommendation is challenging since CQA data are 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 article addresses these issues by a novel statistical model to automatically generate answer keywords in CQA with multiaspect 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.