Exploiting Semantic Query Context to Improve Search Ranking

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.

[1]  Krishna Bharat,et al.  When experts agree: using non-affiliated experts to rank popular topics , 2001, TOIS.

[2]  Wei-Ying Ma,et al.  Probabilistic query expansion using query logs , 2002, WWW '02.

[3]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[4]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[5]  Nicholas J. Belkin,et al.  A case for interaction: a study of interactive information retrieval behavior and effectiveness , 1996, CHI.

[6]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[7]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[8]  Tat-Seng Chua,et al.  Mining dependency relations for query expansion in passage retrieval , 2006, SIGIR.

[9]  Taher H. Haveliwala.  Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..

[10]  Subbarao Kambhampati,et al.  Providing ranked relevant results for web database queries , 2004, WWW Alt. '04.

[11]  Amanda Spink,et al.  An Analysis of Web Documents Retrieved and Viewed , 2003, International Conference on Internet Computing.

[12]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[13]  Wei-Ying Ma,et al.  Query Expansion by Mining User Logs , 2003, IEEE Trans. Knowl. Data Eng..

[14]  Susan T. Dumais,et al.  Improving Web Search Ranking by Incorporating User Behavior Information , 2019, SIGIR Forum.

[15]  Reiner Kraft,et al.  Mining anchor text for query refinement , 2004, WWW '04.

[16]  Benjamin Rey,et al.  Generating query substitutions , 2006, WWW '06.

[17]  In-Ho Kang,et al.  Query type classification for web document retrieval , 2003, SIGIR.

[18]  Andrei Broder,et al.  A taxonomy of web search , 2002, SIGF.

[19]  Daniel E. Rose,et al.  Understanding user goals in web search , 2004, WWW '04.

[20]  Gerard Salton,et al.  Improving retrieval performance by relevance feedback , 1997, J. Am. Soc. Inf. Sci..

[21]  Jaana Kekäläinen,et al.  IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.

[22]  ChengXiang Zhai,et al.  Exploiting query history for document ranking in interactive information retrieval , 2003, SIGIR '03.

[23]  Kristian J. Hammond,et al.  Mining navigation history for recommendation , 2000, IUI '00.

[24]  Eric Horvitz,et al.  Patterns of search: analyzing and modeling Web query refinement , 1999 .

[25]  Eric Brill,et al.  Spelling Correction as an Iterative Process that Exploits the Collective Knowledge of Web Users , 2004, EMNLP.

[26]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[27]  Amnon Shashua,et al.  Ranking with Large Margin Principle: Two Approaches , 2002, NIPS.

[28]  Chris Buckley,et al.  Improving automatic query expansion , 1998, SIGIR '98.

[29]  Hugh E. Williams,et al.  Query expansion using associated queries , 2003, CIKM '03.

[30]  Susan T. Dumais,et al.  Personalizing Search via Automated Analysis of Interests and Activities , 2005, SIGIR.

[31]  Susan T. Dumais,et al.  Optimizing search by showing results in context , 2001, CHI.

[32]  Zhenyu Liu,et al.  Automatic identification of user goals in Web search , 2005, WWW '05.

[33]  ChengXiang Zhai,et al.  Implicit user modeling for personalized search , 2005, CIKM '05.