The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Personalizing search via automated analysis of interests and activities
Abstract
We formulate and study search algorithms that consider a user's prior interactions with a wide variety of content to personalize that user's current Web search. Rather than relying on the unrealistic assumption that people will precisely specify their intent when searching, we pursue techniques that leverage implicit information about the user's interests. This information is used to re-rank Web search results within a relevance feedback framework. We explore rich models of user interests, built from both search-related information, such as previously issued queries and previously visited Web pages, and other information about the user such as documents and email the user has read and created. Our research suggests that rich representations of the user and the corpus are important for personalization, but that it is possible to approximate these representations and provide efficient client-side algorithms for personalizing search. We show that such personalization algorithms can significantly improve on current Web search.
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Cites
IR evaluation methods for retrieving highly relevant documents
Järvelin, K., & Kekäläinen, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130374 · Cited by 1,444
Scaling personalized web search
Jeh, G., & Widom, J. (2003) · Proceedings of the twelfth international conference on World Wide Web - WWW '03 · doi:10.1145/775152.775191 · Cited by 1,195
IR evaluation methods for retrieving highly relevant documents
Järvelin, K., & Kekäläinen, J. (2000) · Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/345508.345545 · Cited by 967
Implicit feedback for inferring user preference: a bibliography
Kelly, D., & Teevan, J. (2003) · ACM SIGIR Forum · doi:10.1145/959258.959260 · Cited by 743
Self-citationStuff I've Seen: A System for Personal Information Retrieval and Re-Use
Dumais, S., Cutrell, E., Cadiz, J. J., Jancke, G., Sarin, R., & Robbins, D. C. (2016) · ACM SIGIR Forum · doi:10.1145/2888422.2888425 · Cited by 691
Self-citationAdaptive web search based on user profile constructed without any effort from users
Sugiyama, K., Hatano, K., & Yoshikawa, M. (2004) · Proceedings of the 13th international conference on World Wide Web · doi:10.1145/988672.988764 · Cited by 645
The perfect search engine is not enough: a study of orienteering behavior in directed search
Teevan, J., Alvarado, C., Ackerman, M. S., & Karger, D. R. (2004) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/985692.985745 · Cited by 548
Self-citationA survey on the use of relevance feedback for information access systems
RUTHVEN, I., & LALMAS, M. (2003) · The Knowledge Engineering Review · doi:10.1017/s0269888903000638 · Cited by 401
Stuff I've seen: a system for personal information retrieval and re-use
Dumais, S., Cutrell, E., Cadiz, J., Jancke, G., Sarin, R., & Robbins, D. C. (2003) · Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval · doi:10.1145/860435.860451 · Cited by 381
Self-citationA case for interaction: a study of interactive information retrieval behavior and effectiveness
Koenemann, J., & Belkin, N. J. (1996) · Proceedings of the SIGCHI conference on Human factors in computing systems common ground - CHI '96 · doi:10.1145/238386.238487 · Cited by 327
Cited by
Chuklin, A., Markov, I., & de Rijke, M. (2015) · Synthesis Lectures on Information Concepts, Retrieval, and Services · doi:10.1007/978-3-031-02294-4 · Cited by 398
Measuring personalization of web search
Hannak, A., Sapiezynski, P., Molavi Kakhki, A., Krishnamurthy, B., Lazer, D., Mislove, A., & Wilson, C. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488435 · Cited by 396
Modeling the impact of short- and long-term behavior on search personalization
Bennett, P. N., White, R. W., Chu, W., Dumais, S. T., Bailey, P., Borisyuk, F., & Cui, X. (2012) · Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/2348283.2348312 · Cited by 300
Self-citationA Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
Nguyen, D. Q., Vu, T., Nguyen, T. D., Nguyen, D. Q., & Phung, D. (2019) · Proceedings of the 2019 Conference of the North · doi:10.18653/v1/n19-1226 · Cited by 267
Large Language Models for Information Retrieval: A Survey
Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Chen, H., Liu, Z., Dou, Z., & Wen, J.-R. (2026) · ACM Transactions on Information Systems · doi:10.1145/3748304 · Cited by 225
Metadata
- Authors
- Teevan, J., Dumais, S. T., & Horvitz, E.
- Venue
- Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2005-08-15
- Pages
- 449-456
- OpenAlex
- W2122841972
- Cited by
- 800
- References
- 18
- FWCI
- 48.04
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Teevan, J., Dumais, S. T., & Horvitz, E. (2005). 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. https://doi.org/10.1145/1076034.1076111
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