Algorithm for recommending answer providers in community-based question answering

Abstract

Obtaining answers from community-based question answering (CQA) services is typically a lengthy process. In this light, the authors propose an algorithm that recommends answer providers. A two-step framework is developed, in which a query likelihood language model is constructed that enables the determination of the interests of answer providers. The model is then used to identify answer providers who are interested in answering questions related to the identified topics. At the same time, a maximum entropy model is designed to estimate answer quality. Finally, an answer-quality-based algorithm is developed to model the expertise of answer providers for the purpose of differentiating answer providers of various capacities. The proposed scheme leverages answer provider interest and expertise, allowing for more effective differentiation. Experiments on real-world data from Baidu Knows, a renowned Chinese CQA service similar to Yahoo! Answers, reveal significant improvements over the baseline methods, and test results demonstrate the effective of the novel approach.