Recommending high utility query via session-flow graph

  • Authors:

  • Xiaofei Zhu;Jiafeng Guo;Xueqi Cheng;Yanyan Lan;Wolfgang Nejdl

  • Affiliations:

  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China,L3S Research Center, Leibniz Universität Hannover, Hannover, Germany;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;L3S Research Center, Leibniz Universität Hannover, Hannover, Germany

  • Venue:
  • ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
  • Year:
  • 2013

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Abstract

Query recommendation is an integral part of modern search engines that helps users find their information needs. Traditional query recommendation methods usually focus on recommending users relevant queries, which attempt to find alternative queries with close search intent to the original query. Whereas the ultimate goal of query recommendation is to assist users to accomplish their search task successfully, while not just find relevant queries in spite of they can sometimes return useful search results. To better achieve the ultimate goal of query recommendation, a more reasonable way is to recommend users high utility queries, i.e., queries that can return more useful information. In this paper, we propose a novel utility query recommendation approach based on absorbing random walk on the session-flow graph, which can learn queries' utility by simultaneously modeling both users' reformulation behaviors and click behaviors. Extensively experiments were conducted on real query logs, and the results show that our method significantly outperforms the state-of-the-art methods under the evaluation metric QRR and MRD.