Improving search relevance for short queries in community question answering

  • Authors:

  • Haocheng Wu;Wei Wu;Ming Zhou;Enhong Chen;Lei Duan;Heung-Yeung Shum

  • Affiliations:

  • University of Science and Technology of China, Hefei, China;Microsoft Research, Beijing, China;Microsoft Research, Beijing, China;University of Science and Technology of China, Hefei, China;Microsoft, SunnyVale, CA, USA;Microsoft Research, Redmond, WA, USA

  • Venue:
  • Proceedings of the 7th ACM international conference on Web search and data mining
  • Year:
  • 2014

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Abstract

Relevant question retrieval and ranking is a typical task in community question answering (CQA). Existing methods mainly focus on long and syntactically structured queries. However, when an input query is short, the task becomes challenging, due to a lack information regarding user intent. In this paper, we mine different types of user intent from various sources for short queries. With these intent signals, we propose a new intent-based language model. The model takes advantage of both state-of-the-art relevance models and the extra intent information mined from multiple sources. We further employ a state-of-the-art learning-to-rank approach to estimate parameters in the model from training data. Experiments show that by leveraging user intent prediction, our model significantly outperforms the state-of-the-art relevance models in question search.