Discovering and using groups to improve personalized search

ABSTRACT

Personalized Web search takes advantage of information about an individual to identify the most relevant results for that person. A challenge for personalization lies in collecting user profiles that are rich enough to do this successfully. One way an individual's profile can be augmented is by using data from other people. To better understand whether groups of people can be used to benefit personalized search, we explore the similarity of query selection, desktop information, and explicit relevance judgments across people grouped in different ways. The groupings we explore fall along two dimensions: the longevity of the group members' relationship, and how explicitly the group is formed. We find that some groupings provide valuable insight into what members consider relevant to queries related to the group focus, but that it can be difficult to identify valuable groups implicitly. Building on these findings, we explore an algorithm to "groupize" (versus "personalize") Web search results that leads to a significant improvement in result ranking on group-relevant queries.

REFERENCES

• 1: Rodrigo B. Almeida , Virgilio A. F. Almeida, A community-aware search engine, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988728]
• 2: Saleema Amershi , Meredith Ringel Morris, CoSearch: a system for co-located collaborative web search, Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, April 05-10, 2008, Florence, Italy [doi>10.1145/1357054.1357311]
• 3: Nicholas J. Belkin , C. Cool , W. Bruce Croft , James P. Callan, The effect multiple query representations on information retrieval system performance, Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, p.339-346, June 27-July 01, 1993, Pittsburgh, Pennsylvania, United States [doi>10.1145/160688.160760]
• 4: Zhicheng Dou , Ruihua Song , Ji-Rong Wen, A large-scale evaluation and analysis of personalized search strategies, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242651]
• 5: Freyne, J. and Smyth, B. (2006). Cooperating search communities. In Proc. of AH '06, 101--110.
• 6: Kalervo Järvelin , Jaana Kekäläinen, IR evaluation methods for retrieving highly relevant documents, Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, p.41-48, July 24-28, 2000, Athens, Greece [doi>10.1145/345508.345545]
• 7: Rosie Jones , Ravi Kumar , Bo Pang , Andrew Tomkins, "I know what you did last summer": query logs and user privacy, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, November 06-10, 2007, Lisbon, Portugal [doi>10.1145/1321440.1321573]
• 8: Young-Jin Lee, VisSearch: a collaborative Web searching environment, Computers & Education, v.44 n.4, p.423-439, May 2005 [doi>10.1016/j.compedu.2004.04.009]
• 9: Qiaozhu Mei , Kenneth Church, Entropy of search logs: how hard is search? with personalization? with backoff?, Proceedings of the international conference on Web search and web data mining, February 11-12, 2008, Palo Alto, California, USA [doi>10.1145/1341531.1341540]
• 10: Meredith Ringel Morris , Jaime Teevan , Steve Bush, Enhancing collaborative web search with personalization: groupization, smart splitting, and group hit-highlighting, Proceedings of the ACM 2008 conference on Computer supported cooperative work, November 08-12, 2008, San Diego, CA, USA [doi>10.1145/1460563.1460640]
• 11: Meredith Ringel Morris, A survey of collaborative web search practices, Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, April 05-10, 2008, Florence, Italy [doi>10.1145/1357054.1357312]
• 12: Meredith Ringel Morris , Eric Horvitz, SearchTogether: an interface for collaborative web search, Proceedings of the 20th annual ACM symposium on User interface software and technology, October 07-10, 2007, Newport, Rhode Island, USA [doi>10.1145/1294211.1294215]
• 13: Mark O'Connor , Dan Cosley , Joseph A. Konstan , John Riedl, PolyLens: a recommender system for groups of users, Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work, p.199-218, September 16-20, 2001, Bonn, Germany
• 14: Jeremy Pickens , Gene Golovchinsky , Chirag Shah , Pernilla Qvarfordt , Maribeth Back, Algorithmic mediation for collaborative exploratory search, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390389]
• 15: James Pitkow , Hinrich Schütze , Todd Cass , Rob Cooley , Don Turnbull , Andy Edmonds , Eytan Adar , Thomas Breuel, Personalized search, Communications of the ACM, v.45 n.9, September 2002 [doi>10.1145/567498.567526]
• 16: Xuehua Shen , Bin Tan , ChengXiang Zhai, Implicit user modeling for personalized search, Proceedings of the 14th ACM international conference on Information and knowledge management, October 31-November 05, 2005, Bremen, Germany [doi>10.1145/1099554.1099747]
• 17: Barry Smyth, A Community-Based Approach to Personalizing Web Search, Computer, v.40 n.8, p.42-50, August 2007 [doi>10.1109/MC.2007.259]
• 18: Sparck Jones, K., Walker, S., and Robertson, S. A. (1998). Probabilistic model of information retrieval: Development and status. TR-446, Cambridge University Computer Laboratory.
• 19: Amanda Spink , Bernard J. Jansen, Web Search: Public Searching of the Web (Information Science and Knowledge Management), Springer-Verlag New York, Inc., Secaucus, NJ, 2005
• 20: 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]
• 21: Jian-Tao Sun , Hua-Jun Zeng , Huan Liu , Yuchang Lu , Zheng Chen, CubeSVD: a novel approach to personalized Web search, Proceedings of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1060745.1060803]
• 22: Jaime Teevan , Susan T. Dumais , Eric Horvitz, Personalizing search via automated analysis of interests and activities, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076111]
• 23: Teevan, J., Dumais, S. T., and Horvitz, E. (2005). Beyond the commons: On the value of personalizing Web search. In Proc. of PIA '05 Workshop, 84--92.
• 24: Michael B. Twidale , David M. Nichols , Chris D. Paice, Browsing is a collaborative process, Information Processing and Management: an International Journal, v.33 n.6, p.761-783, Nov. 1997 [doi>10.1016/S0306-4573(97)00040-X]
• 25: Voorhees, E. and Harman, D. (Eds.) (2005). TREC: Experimental Evaluation of Information Retrieval. MIT Press.

INDEX TERMS

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

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.3 Group and Organization Interfaces
          Subjects: Computer-supported cooperative work

General Terms:
Algorithms, Experimentation, Human Factors, Measurement

Keywords:
collaborative filtering, collaborative search, personalization