Improving search relevance for short queries in community question answering

Haocheng Wu, Wei Wu, Ming Zhou, Enhong Chen, Lei Duan, Heung-Yeung Shum
2014 Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14  
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
more » ... age 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-torank 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.
doi:10.1145/2556195.2556239 dblp:conf/wsdm/WuWZCDS14 fatcat:qcgoeb3ulbdgvlyvcd3tonlapi