Find-similar: similarity browsing as a search tool

ABSTRACT

Search systems have for some time provided users with the ability to request documents similar to a given document. Interfaces provide this feature via a link or button for each document in the search results. We call this feature find-similar or similarity browsing. We examined find-similar as a search tool, like relevance feedback, for improving retrieval performance. Our investigation focused on find-similar's document-to-document similarity, the reexamination of documents during a search, and the user's browsing pattern. Find-similar with a query-biased similarity, avoiding the reexamination of documents, and a breadth-like browsing pattern achieved a 23% increase in the arithmetic mean average precision and a 66% increase in the geometric mean average precision over our baseline retrieval. This performance matched that of a more traditionally styled iterative relevance feedback technique.

REFERENCES

• 1: IJsbrand Jan Aalbersberg, Incremental relevance feedback, Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, p.11-22, June 21-24, 1992, Copenhagen, Denmark [doi>10.1145/133160.133169]
• 2: James Allan, Automatic hypertext construction, Cornell University, Ithaca, NY, 1996
• 3: C. Buckley. trec_eval. http://trec.nist.gov/trec_eval/trec_eval.8.0.tar.gz.
• 4: I. Campbell. The ostensive model of developing information needs. PhD thesis, University of Glasgow, 2000.
• 5: Hao Chen , Susan Dumais, Bringing order to the Web: automatically categorizing search results, Proceedings of the SIGCHI conference on Human factors in computing systems, p.145-152, April 01-06, 2000, The Hague, The Netherlands [doi>10.1145/332040.332418]
• 6: Paul R. Cohen, Empirical methods for artificial intelligence, MIT Press, Cambridge, MA, 1995
• 7: W. B. Croft. What do people want from information retrieval? D-Lib Magazine, Nov. 1995.
• 8: S. T. Dumais and D. G. Schmitt. Iterative searching in an online database. In Proc. Human Factors Soc. 35th Annual Mtg., pages 398--402, 1991.
• 9: Koji Eguchi, Adaptive cluster-based browsing using incrementally expanded queries and its effects (poster abstract), Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.265-266, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312688]
• 10: M. Hancock-Beaulieu, M. Fieldhouse, and T. Do. An evaluation of interactive query expansion in an online library catalogue with a graphical user interface. Journal of Documentation, 51(3):225--243, 1995.
• 11: Donna Harman, Relevance feedback revisited, Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, p.1-10, June 21-24, 1992, Copenhagen, Denmark [doi>10.1145/133160.133167]
• 12: Marti A. Hearst , Jan O. Pedersen, Reexamining the cluster hypothesis: scatter/gather on retrieval results, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.76-84, August 18-22, 1996, Zurich, Switzerland [doi>10.1145/243199.243216]
• 13: Makoto Iwayama, Relevance feedback with a small number of relevance judgements: incremental relevance feedback vs. document clustering, Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, p.10-16, July 24-28, 2000, Athens, Greece [doi>10.1145/345508.345538]
• 14: Kerstin Klöckner , Nadine Wirschum , Anthony Jameson, Depth- and breadth-first processing of search result lists, CHI '04 extended abstracts on Human factors in computing systems, April 24-29, 2004, Vienna, Austria [doi>10.1145/985921.986115]
• 15: Jürgen Koenemann , Nicholas J. Belkin, A case for interaction: a study of interactive information retrieval behavior and effectiveness, Proceedings of the SIGCHI conference on Human factors in computing systems: common ground, p.205-212, April 13-18, 1996, Vancouver, British Columbia, Canada [doi>10.1145/238386.238487]
• 16: Robert Krovetz, Viewing morphology as an inference process, Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, p.191-202, June 27-July 01, 1993, Pittsburgh, Pennsylvania, United States [doi>10.1145/160688.160718]
• 17: Victor Lavrenko , W. Bruce Croft, Relevance based language models, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.120-127, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383972]
• 18: Lemur Toolkit for Language Modeling and IR. http://www.lemurproject.org/.
• 19: Anton Leuski, Relevance and reinforcement in interactive browsing, Proceedings of the ninth international conference on Information and knowledge management, p.119-126, November 06-11, 2000, McLean, Virginia, United States [doi>10.1145/354756.354809]
• 20: H. Lieberman. Letizia: An agent that assists web browsing. In IJCAI-95, pages 924--929, 1995.
• 21: Donald Metzler , W. Bruce Croft, Combining the language model and inference network approaches to retrieval, Information Processing and Management: an International Journal, v.40 n.5, p.735-750, September 2004 [doi>10.1016/j.ipm.2004.05.001]
• 22: Donald Metzler , W. Bruce Croft, A Markov random field model for term dependencies, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076115]
• 23: D. Metzler, F. Diaz, T. Strohman, and W. B. Croft. UMass robust 2005 notebook: Using mixtures of relevance models for query expansion. In TREC 2005 Notebook, 2005.
• 24: Christopher Olston , Ed H. Chi, ScentTrails: Integrating browsing and searching on the Web, ACM Transactions on Computer-Human Interaction (TOCHI), v.10 n.3, p.177-197, September 2003 [doi>10.1145/937549.937550]
• 25: Jay M. Ponte , W. Bruce Croft, A language modeling approach to information retrieval, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.275-281, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.291008]
• 26: Pubmed, www.pubmed.gov. "Related articles": www.nlm.nih.gov/bsd/pubmed_tutorial/m5002.html.
• 27: J. J. Rocchio. Relevance feedback in information retrieval. In G. Salton, editor, The SMART Retrieval System, pages 313--323. Prentice Hall, 1971.
• 28: Ian Ruthven , Mounia Lalmas, A survey on the use of relevance feedback for information access systems, The Knowledge Engineering Review, v.18 n.2, p.95-145, June 2003 [doi>10.1017/S0269888903000638]
• 29: Mark Sanderson, Accurate user directed summarization from existing tools, Proceedings of the seventh international conference on Information and knowledge management, p.45-51, November 02-07, 1998, Bethesda, Maryland, United States [doi>10.1145/288627.288640]
• 30: J. G. Siek, L.-Q. Lee, and A. Lumsdaine. The Boost Graph Library. Addison Wesley, 2001.
• 31: A. Spink, B. J. Jansen, and H. C. Ozmultu. Use of query reformulation and relevance feedback by excite users. Internet Research: Electronic Networking Applications and Policy, 10(4):317--328, 2000.
• 32: Amanda Spink , Dietmar Wolfram , Major B. J. Jansen , Tefko Saracevic, Searching the Web: the public and their queries, Journal of the American Society for Information Science and Technology, v.52 n.3, p.226-234, Feb. 1, 2001 [doi>10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.3.CO;2-I]
• 33: T. Strohman, D. Metzler, H. Turtle, and W. B. Croft. Indri: A language-model based search engine for complex queries (extended version). Technical Report IR-407, CIIR, UMass, 2005.
• 34: R. H. Thompson , W. B. Croft, Support for browsing in an intelligent text retrieval system, International Journal of Man-Machine Studies, v.30 n.6, p.639-668, June 1989
• 35: A. Tombros. The effectiveness of query-based hierarchic clustering of documents for information retrieval. PhD thesis, University of Glasgow, 2002.
• 36: Anastasios Tombros , Mark Sanderson, Advantages of query biased summaries in information retrieval, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.2-10, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.290947]
• 37: Howard Turtle , W. Bruce Croft, Evaluation of an inference network-based retrieval model, ACM Transactions on Information Systems (TOIS), v.9 n.3, p.187-222, July 1991 [doi>10.1145/125187.125188]
• 38: Ellen M. Voorhees, The TREC robust retrieval track, ACM SIGIR Forum, v.39 n.1, June 2005 [doi>10.1145/1067268.1067272]
• 39: E. M. Voorhees and H. T. Dang. Draft: Overview of the TREC 2005 robust retrieval track. In TREC 2005 Notebook, pages 105--112, 2005.
• 40: R. W. White, J. M. Jose, C. J. van Rijsbergen, and I. Ruthven. A simulated study of implicit feedback models. In ECIR, 2004.
• 41: W. John Wilbur , Leona Coffee, The effectiveness of document neighboring in search enhancement, Information Processing and Management: an International Journal, v.30 n.2, p.253-266, March-April 1994 [doi>10.1016/0306-4573(94)90068-X]
• 42: 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

General Terms:
Algorithms, Experimentation

Keywords:
find-similar, relevance feedback, similarity browsing