Yabo Xu
ABSTRACT
Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, users are uncomfortable with exposing private preference information to search engines. On the other hand, privacy is not absolute, and often can be compromised if there is a gain in service or profitability to the user. Thus, a balance must be struck between search quality and privacy protection. This paper presents a scalable way for users to automatically build rich user profiles. These profiles summarize a user.s interests into a hierarchical organization according to specific interests. Two parameters for specifying privacy requirements are proposed to help the user to choose the content and degree of detail of the profile information that is exposed to the search engine. Experiments showed that the user profile improved search quality when compared to standard MSN rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by only sharing some higher-level user profile information, which is potentially less sensitive than detailed personal information.
- J. Pitkow, H. Schuetze, T. Cass, R. Cooley, D. Turnbull, A. Edmonds, E. Adar, and T. Breuel. Personalized search. Communications of the ACM, 45(9):50--55, 2002. Google Scholar
Digital Library
- Google personalized search: http://www.google.com/psearchGoogle Scholar
- Yahoo! My Web 2.0: http://myweb2.search.yahoo.com/Google Scholar
- W. Gasarch. A survey on private information retrieval. The bulletin of the European Association for Theoretical Computer Science (EATCS), 82:72--107, 2004.Google Scholar
- Glen Jeh, and Jennifer Widom. Scaling personalized web search. In Proc. of the 12th International World Wide Web Conference (WWW), Budapest, Hungary, May 2003. Google ScholarDigital Library
- T. H. Haveliwala. Topic-sensitive PageRank. In Proc. of the 11th International World Wide Web Conference (WWW), Honolulu, Hawaii, May 2002. Google ScholarDigital Library
- K. Sugiyama, K. Hatano and M. Yoshikawa. Adaptive Web search based on user profile constructed without any effort from users, In Proc. of the 13th International World Wide Web Conference (WWW), New York, New York, May 2004. Google ScholarDigital Library
- J. Teevan, S. T. Dumais, and Eric Horvitz. Personalizing search via automated analysis of interests and activities. In the Prof. of 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Salvador, Brazil. August, 2005. Google ScholarDigital Library
- Paolo Ferragina, and Antonio Gulli. A personalized search engine based on Web-Snippet hierarchical clustering. In Proc. of the 14th International World Wide Web Conference (WWW), Chiba, Japan, May 2005. Google ScholarDigital Library
- P. A. Chirita, W. Nejdl, R. Paiu, and C. Kohlschutter. Using ODP metadata to personalize search. In the Prof. of 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Salvador, Brazil, August, 2005. Google ScholarDigital Library
- H. R. Kim, and Philip K. Chan. Learning implicit user interest hierarchy for context in personalization. In Proc. of International Conference on Intelligent User Interface (IUI), Miami, Florida, January, 2003. Google ScholarDigital Library
- M. Speretta, and S. Gauch, Personalizing search based on user search history. In Proc. of International Conference of Knowledge Management(CIKM), Washington D.C., 2004.Google Scholar
- P. Anick. Using terminological feed back for Web search refinement: a log-based study. In Proc. of the 13th International World Wide Web Conference (WWW), New York, New York, May 2004.Google Scholar
- K. R. McKeown, N. Elhadad, and V. Hatzivassiloglou. Leveraging a common representation for personalized search and summarization in a medical digital library. In Proc. of International Conference on Digital Library, 2003. Google ScholarDigital Library
- A. Kritikopoulos, and M. Sideri. The compass Filter: Search engine result personalization using web communities. In Proc. of Intelligent Techniques in Web Personalization (ITWP), 2003. Google ScholarDigital Library
- B. Fung, K. Wang and M. Ester. Hierarchical document clustering using frequent itemsets. In Proc. Of SIAM International Conference on Data Mining, San Francisco, May 2003.Google ScholarCross Ref
- K. Wang, C. Xu, B. Ling, "Clustering transactions using large items", In Proc. of the 8th Conference on Information and Knowledge Management (CIKM), Kansas City, November, 1999. Google ScholarDigital Library
- J. Sun, H. Zeng, H. Liu, Y. Lu, and Z. Chen. CubeSVD: A Novel Approach to Personalized Web Search. In Proc. of the 14th International World Wide Web Conference (WWW), Chiba, Japan, May 2005. Google ScholarDigital Library
- R. Agrawal, and R. Skriant. Privacy preserving data mining. In Proc. of the ACM SIGMOD Conference on Management of Data (SIGMOD), Dallas, Texas, May 2000. Google ScholarDigital Library
- A. Evfimievski, J. Gehrke and R. Srikant. Limiting privacy breaches in privacy preserving data mining. In Proc. of the ACM SIGMOD/PODS(PODS), San Diego, CA, 2003. Google ScholarDigital Library
- L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 557--570. Google ScholarDigital Library
- R. Baeza-Yates, and B. Ribeiro-Neto, Modern Information Retrieval. Addison Wesley Longman, MA, 1999. Google ScholarDigital Library
- W. Alan. Privacy and Freedom. Atheneum Press, Boston, 1967.Google Scholar
- J. Carroll and M. Rosson. The paradox of the active user. In J. M. Carroll (Ed.), Interfacing Thought: Cognitive Aspects of Human-Computer interaction, MIT Press, Cambridge, 1987. Google ScholarDigital Library
- M. Tribus. Thermostatics and Thermodynamics, D. Van Nostrand, New York, NY, 1961.Google Scholar
- T. M. Cover and J. A. Thomas. Elements of Information Theory, 1st Edition. Wiley-InterScience, New York, NY, 1991. Google ScholarDigital Library
- J. Han. Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA, 2001. Google ScholarDigital Library
- F. Qiu, and J. Cho. Automatic identification of user interest for personalized search. In Proc. of the 12th International World Wide Web Conference (WWW), Edinburgh, Scotland, May 2006. Google ScholarDigital Library
Index Terms
- Privacy-enhancing personalized web search
Recommendations
Topic Model based Privacy Protection in Personalized Web Search
SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information RetrievalModern search engines utilize users' search history for personalization, which provides more effective, useful and relevant search results. However, it also has the potential risk of revealing users' privacy by identifying their underlying intention from ...
FedPS: A Privacy Protection Enhanced Personalized Search Framework
WWW '21: Proceedings of the Web Conference 2021Personalized search returns each user more accurate results by collecting the user’s historical search behaviors to infer her interests and query intents. However, it brings the risk of user privacy leakage, and this may greatly limit the practical ...
Automatic identification of user interest for personalized search
WWW '06: Proceedings of the 15th international conference on World Wide WebOne hundred users, one hundred needs. As more and more topics are being discussed on the web and our vocabulary remains relatively stable, it is increasingly difficult to let the search engine know what we want. Coping with ambiguous queries has long ...
Comments