Publications
Time-dependent semantic similarity measure of queries using historical click-through data
Zhao, Q.; Hoi, S. C. H.; Liu, T.-Y.; Bhowmick, S. S.; Lyu, M. R. & Ma, W.-Y.
, 'WWW '06: Proceedings of the 15th international conference on World Wide Web', ACM Press, New York, NY, USA, [http://doi.acm.org/10.1145/1135777.1135858], 543-552 (2006) [pdf]
It has become a promising direction to measure similarity of Web search queries by mining the increasing amount of click-through data logged by Web search engines, which record the interactions between users and the search engines. Most existing approaches employ the click-through data for similarity measure of queries with little consideration of the temporal factor, while the click-through data is often dynamic and contains rich temporal information. In this paper we present a new framework of time-dependent query semantic similarity model on exploiting the temporal characteristics of historical click-through data. The intuition is that more accurate semantic similarity values between queries can be obtained by taking into account the timestamps of the log data. With a set of user-defined calendar schema and calendar patterns, our time-dependent query similarity model is constructed using the marginalized kernel technique, which can exploit both explicit similarity and implicit semantics from the click-through data effectively. Experimental results on a large set of click-through data acquired from a commercial search engine show that our time-dependent query similarity model is more accurate than the existing approaches. Moreover, we observe that our time-dependent query similarity model can, to some extent, reflect real-world semantics such as real-world events that are happening over time.