@inproceedings{4597173,
  abstract = {One challenge for relevance ranking in Web search is underspecified queries. For such queries, top-ranked documents may contain information irrelevant to the search goal of the user; some newly-created relevant documents are ranked lower due to their freshness and to the large number of existing documents that match the queries. To improve the relevance ranking for underspecified queries requires better understanding of users' search goals. By analyzing the semantic query context extracted from the query logs, we propose Q-Rank to effectively improve the ranking of search results for a given query. Experiments show that Q-Rank outperforms the current ranking system of a large-scale commercial Web search engine, improving the relevance ranking for 82% of the queries with an average increase of 8.99% in terms of discounted cumulative gains. Because Q-Rank is independent of the underlying ranking algorithm, it can be integrated with existing search engines.},
  author = {Zhuang, Z. and Cucerzan, S.},
  doi = {10.1109/ICSC.2008.8},
  interhash = {fd70fee1920ea227a8c336fe80e2ba71},
  intrahash = {8c2005e1dea667cdd23a8e5c7efe9243},
  journal = {Semantic Computing, 2008 IEEE International Conference on},
  month = {Aug.},
  pages = {50-57},
  title = {Exploiting Semantic Query Context to Improve Search Ranking},
  year = 2008
}

@inproceedings{1281204,
  address = {New York, NY, USA},
  author = {Baeza-Yates, Ricardo and Tiberi, Alessandro},
  booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {http://doi.acm.org/10.1145/1281192.1281204},
  interhash = {26ca034be705abaf072835784f53d877},
  intrahash = {6e45b65feffd1545c6dca62bf4b8f53d},
  isbn = {978-1-59593-609-7},
  location = {San Jose, California, USA},
  pages = {76--85},
  publisher = {ACM},
  title = {Extracting semantic relations from query logs},
  url = {http://portal.acm.org/citation.cfm?id=1281204},
  year = 2007
}

@inproceedings{1135858,
  abstract = {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.},
  address = {New York, NY, USA},
  author = {Zhao, Qiankun and Hoi, Steven C. H. and Liu, Tie-Yan and Bhowmick, Sourav S. and Lyu, Michael R. and Ma, Wei-Ying},
  booktitle = {WWW '06: Proceedings of the 15th international conference on World Wide Web},
  doi = {http://doi.acm.org/10.1145/1135777.1135858},
  interhash = {c765e101c37f6b530e2c1c59808048d7},
  intrahash = {57cbc64550d3a1b5b8599a0783e95111},
  isbn = {1-59593-323-9},
  location = {Edinburgh, Scotland},
  pages = {543--552},
  publisher = {ACM Press},
  title = {Time-dependent semantic similarity measure of queries using historical click-through data},
  url = {http://portal.acm.org/citation.cfm?id=1135858},
  year = 2006
}