CONFERENCE (INTERNATIONAL) Quantifying Query Ambiguity with Topic Distributions

Yuki Yano, Yukihiro Tagami, Akira Tajima

The 25th ACM International Conference on Information and Knowledge Management (CIKM2016)

October 24, 2016

Query ambiguity is a useful metric for search engines to understand users’ intents. Existing methods quantify query ambiguity by calculating an entropy of clicks. These methods assign each click to a one-hot vector corresponding to some mutually exclusive groups. However, they cannot incorporate non-obvious structures such as similarity among documents. In this paper, we propose a new approach for quantifying query ambiguity using topic distributions. We show that it is a natural extension of an existing entropy-based method. Further, we use our approach to achieve topic-based extensions of major existing entropy-based methods. Through an evaluation using e-commerce search logs combined with human judgments, our approach successfully extended existing entropy-based methods and improved the quality of query ambiguity measurements.

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