Scalable Algorithms for CQA Post Voting Prediction - 2017


Community Question Answering (CQA) sites, such as Stack Overflow and Yahoo! Answers, became terribly in style in recent years. These sites contain rich crowdsourcing information contributed by the site users in the form of questions and answers, and these questions and answers can satisfy the data needs of more users. In this text, we tend to aim at predicting the voting countless queries/answers shortly when they're posted in the CQA sites. To accomplish this task, we determine 3 key aspects that matter with the voting of a post, i.e., the non-linear relationships between features and output, the question and answer coupling, and also the dynamic fashion of information arrivals. A family of algorithms are proposed to model the on top of 3 key aspects. Some approximations and extensions are proposed to scale up the computation. We analyze the proposed algorithms in terms of optimality, correctness, and complexity. Extensive experimental evaluations conducted on 2 real knowledge sets demonstrate the effectiveness and efficiency of our algorithms.

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