Diversifying Question Recommendations in Community-Based Question Answering

Yaoyun Zhang, Xiaolong Wang, Xuan Wang, Ruifeng Xu, Buzhou Tang

Abstract: Question retrieval is an important research topic in community-based question answering (QA). Conventionally, questions semantically equivalent to the query question are considered as top ranks. However, traditional question retrieval technique has the difficulty to process the users’ information needs which are implicitly embedded in the question. This paper proposes a novel method of question recommendation by considering user’s diverse information needs. By estimating information need compactness in the question retrieval results, we further identify the retrieval results need to be diversified. For these results, the scores of information retrieval model, the importance and novelty of both question types and the informational aspects of question content, are combined to do diverse question recommendation. Comparative experiments on a large scale real community-based QA dataset show that the proposed method effectively improves information need coverage and diversity through relevant questions recommendation.