The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Implicit interest indicators
Abstract
Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can ``intelligently'' determine the interest of a user and use this information to make suggestions. The common solution, ``explicit ratings'', where users tell the system what they think about a piece of information, is well-understood and fairly precise. However, having to stop to enter explicit ratings can alter normal patterns of browsing and reading. A more ``intelligent'' method is to useimplicit ratings, where a rating is obtained by a method other than obtaining it directly from the user. These implicit interest indicators have obvious advantages, including removing the cost of the user rating, and that every user interaction with the system can contribute to an implicit rating.
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Cites
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GroupLens: applying collaborative filtering to Usenet news
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Groupware and social dynamics: eight challenges for developers
Grudin, J. (1994) · Communications of the ACM · doi:10.1145/175222.175230 · Cited by 1,228
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Hill, W., Stead, L., Rosenstein, M., & Furnas, G. (1995) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '95 · doi:10.1145/223904.223929 · Cited by 1,129
Combining Content-Based and Collaborative Filters in an Online Newspaper
Claypool, M. K., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., & Sartin, M. M. (1999) · Cited by 775
Self-citationHill, W. C., Hollan, J. D., Wroblewski, D., & McCandless, T. (1992) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '92 · doi:10.1145/142750.142751 · Cited by 509
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Implicit Feedback for Recommender Systems
Oard, D. W., & Kim, J. (1998) · Scholar Commons (University of South Carolina) · Cited by 371
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Cited by
Collaborative topic modeling for recommending scientific articles
Wang, C., & Blei, D. M. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020480 · Cited by 1,527
Accurately interpreting clickthrough data as implicit feedback
Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2005) · Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1076034.1076063 · Cited by 1,408
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
Joachims, T., Granka, L., Pan, B., Hembrooke, H., Radlinski, F., & Gay, G. (2007) · ACM Transactions on Information Systems · doi:10.1145/1229179.1229181 · Cited by 677
Personalized Search Based on User Search Histories
Speretta, M., & Gauch, S. (2005) · The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05) · doi:10.1109/wi.2005.114 · Cited by 384
Accurately Interpreting Clickthrough Data as Implicit Feedback
Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2017) · ACM SIGIR Forum · doi:10.1145/3130332.3130334 · Cited by 212
Metadata
- Authors
- Claypool, M., Le, P., Wased, M., & Brown, D.
- Venue
- Proceedings of the 6th international conference on Intelligent user interfaces
- Publisher
- ACM
- Published
- 2001-01
- Pages
- 33-40
- OpenAlex
- W2074680184
- Cited by
- 666
- References
- 13
- FWCI
- 15.28
- Type
- conference paper
- Language
- en
- Open access
- closed
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