Context-sensitive information retrieval using implicit feedback

ABSTRACT

A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.

REFERENCES

• 1: Eytan Adar , David Kargar , Lynn Andrea Stein, Haystack: per-user information environments, Proceedings of the eighth international conference on Information and knowledge management, p.413-422, November 02-06, 1999, Kansas City, Missouri, United States [doi>10.1145/319950.323231]
• 2: J. Allan and et al. Challenges in information retrieval and language modeling. Workshop at University of Amherst, 2002.
• 3: Krishna Bharat, SearchPad: explicit capture of search context to support Web search, Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking, p.493-501, June 2000, Amsterdam, The Netherlands
• 4: W. B. Croft, S. Cronen-Townsend, and V. Larvrenko. Relevance feedback and personalization: A language modeling perspective. In Proeedings of Second DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries, 2001.
• 5: Hang Cui , Ji-Rong Wen , Jian-Yun Nie , Wei-Ying Ma, Probabilistic query expansion using query logs, Proceedings of the 11th international conference on World Wide Web, May 07-11, 2002, Honolulu, Hawaii, USA [doi>10.1145/511446.511489]
• 6: Susan Dumais , Edward Cutrell , Raman Sarin , Eric Horvitz, Implicit queries (IQ) for contextualized search, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009137]
• 7: Lev Finkelstein , Evgeniy Gabrilovich , Yossi Matias , Ehud Rivlin , Zach Solan , Gadi Wolfman , Eytan Ruppin, Placing search in context: the concept revisited, Proceedings of the 10th international conference on World Wide Web, p.406-414, May 01-05, 2001, Hong Kong, Hong Kong [doi>10.1145/371920.372094]
• 8: C. Huang, L. Chien, and Y. Oyang. Query session based term suggestion for interactive web search. In Proceedings of WWW 2001, 2001.
• 9: Xiangji Huang , Fuchun Peng , Aijun An , Dale Schuurmans, Dynamic web log session identification with statistical language models, Journal of the American Society for Information Science and Technology, v.55 n.14, p.1290-1303, December 2004 [doi>10.1002/asi.20084]
• 10: Glen Jeh , Jennifer Widom, Scaling personalized web search, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775191]
• 11: Thorsten Joachims, Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775067]
• 12: Diane Kelly , Nicholas J. Belkin, Display time as implicit feedback: understanding task effects, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009057]
• 13: Diane Kelly , Jaime Teevan, Implicit feedback for inferring user preference: a bibliography, ACM SIGIR Forum, v.37 n.2, p.18-28, Fall 2003 [doi>10.1145/959258.959260]
• 14: J. Rocchio. Relevance feedback information retrieval. In The Smart Retrieval System-Experiments in Automatic Document Processing, pages 313--323, Kansas City, MO, 1971. Prentice-Hall.
• 15: Xuehua Shen , Cheng Xiang Zhai, Exploiting query history for document ranking in interactive information retrieval, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860509]
• 16: Smitha Sriram , Xuehua Shen , Chengxiang Zhai, A session-based search engine, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009086]
• 17: Kazunari Sugiyama , Kenji Hatano , Masatoshi Yoshikawa, Adaptive web search based on user profile constructed without any effort from users, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988764]
• 18: R. W. White, J. M. Jose, C. J. van Rijsbergen, and I. Ruthven. A simulated study of implicit feedback models. In Proceedings of ECIR 2004, pages 311--326, 2004.
• 19: C. Zhai and J. Lafferty. Model-based feedback in the KL-divergence retrieval model. In Proceedings of CIKM 2001, 2001.
• 20: Chengxiang Zhai , John Lafferty, A study of smoothing methods for language models applied to Ad Hoc information retrieval, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.334-342, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.384019]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

General Terms:
Algorithms

Keywords:
context, interactive retrieval, query expansion, query history