ABSTRACT
Major search engines currently use the history of a user's actions (e.g., queries, clicks) to personalize search results. In this paper, we present a new personalized service, query-specific web recommendations (QSRs), that retroactively answers queries from a user's history as new results arise. The QSR system addresses two important subproblems with applications beyond the system itself: (1) Automatic identification of queries in a user's history that represent standing interests and unfulfilled needs. (2) Effective detection of interesting new results to these queries. We develop a variety of heuristics and algorithms to address these problems, and evaluate them through a study of Google history users. Our results strongly motivate the need for automatic detection of standing interests from a user's history, and identifies the algorithms that are most useful in doing so. Our results also identify the algorithms, some which are counter-intuitive, that are most useful in identifying interesting new results for past queries, allowing us to achieve very high precision over our data set.
- Amazon website. http://www.amazon.com.Google Scholar
- S. Babu and J. Widom. Continuous queries over data streams. In SIGMOD Record, September 2001. Google ScholarDigital Library
- J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of the Conference on Uncertainty in Artifical Intelligence, 1998. Google ScholarDigital Library
- J. Chen, D. DeWitt, F. Tian, and Y. Wang. Niagaracq: A scalable continuous query system for internet databases. In Proc. of SIGMOD, 2000. Google ScholarDigital Library
- D. Goldberg, D. Nichols, B. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. In Communications of the ACM, December 1992. Google ScholarDigital Library
- Google website. http://www.google.com.Google Scholar
- Google Web Alerts. http://www.google.com/alerts.Google Scholar
- J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR, August 1999. Google ScholarDigital Library
- G. Jeh and J. Widom. Scaling personalized web search. In Proc. of WWW 2003, May 2003. Google ScholarDigital Library
- U. Lee, Z. Liu, and J. Cho. Automatic identification of user goals in web search. In Proc. of WWW 2005, May 2005. Google ScholarDigital Library
- F. Liu, C. Yu, and W. Meng. Personalized web search by mapping user queries to categories. In Proc. of the Conference on Information and Knowledge Management, November 2002. Google ScholarDigital Library
- S. Madden, M. Shah, J. Hellerstein, and J. Raman. Continuously adaptive continuous queries over streams. In Proc. of SIGMOD, 2002. Google ScholarDigital Library
- P. Melville, R. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommenadtions. In Proc. of the Conference on Artifical Intelligence, July 2002. Google ScholarDigital Library
- J. H. Hwanga nd M. Balazinska, A. Rasin, U. Cetintemel, M. Stonebraker, and S. Zdonik. High availability algorithms for distributed stream processing. In Proc. of the 21st International Conference on Data Engineering, April 2005. Google ScholarDigital Library
- J. Pitkow, H. Schutze, T. Cass, R. Cooley, D. Turnbull, A. Edmonds, E. Edar, and T. Breuel. Personalized search. In Communications of the ACM, 45(9):50--55, 2002. Google ScholarDigital Library
- A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proc. of the Conference on Uncertainty in Artifical Intelligence, 2001. Google ScholarDigital Library
- D. Rose and D. Levinson. Understanding user goals in web search. In World Wide Web Conference (WWW), 2004. 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 WWW, 2004. 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 WWW 2005, 2005 May. Google ScholarDigital Library
- Yahoo website. http://www.yahoo.com.Google Scholar
- B. Yang and G. Jeh. Retroactive answering of search queries. Technical report, 2006. Extended version, available upon request.Google Scholar
Index Terms
- Retroactive answering of search queries
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