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Interacting with recommender systems

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Patrick Baudisch,

Loren TerveenAuthors Info & Claims

CHI EA '99: CHI '99 Extended Abstracts on Human Factors in Computing Systems

Page 164

https://doi.org/10.1145/632716.632815

Published: 15 May 1999 Publication History

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Abstract

Many people today live in information-rich worlds, constantly facing the question: what should I do next? Which papers should I read to learn about a new area I am interested in? Which movie should I go to? Which restaurant would I like? The experience of friends and colleagues is a valuable resource for making such decisions, especially friends who are familiar with the subject area and have similar tastes.The field of recommender systems (or collaborative filtering) attempts to automate this process, e.g., by supporting people in making recommendations, finding a set of people who are likely to provide good recommendations for a given person, or deriving recommendations from implicit behavior such as browsing activity, buying patterns, and time on task.

References

[1]

Konstan J. A., Riedl, J., Borchers, A. Herlocker, J. L. Recommender Systems: A GroupLens Perspective, AAAI Workshop Recommender Systems 98, Papers from the 1998 Workshop Technical report WS-98-08, AAAI Press, Menlo Park California, 1998

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[2]

The March 1997 issue of Communications of the ACM, edited by Hal Varian and Paul Resnick.

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[3]

The Collaborative Filtering Resource web page, at http://www.sims.berkeley.edu/resources/collab

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Cited By

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Fan HHussain FHussain O(2015)Semantic client-side approach for web personalization of SaaS-based cloud servicesConcurrency and Computation: Practice & Experience10.1002/cpe.341827:8(2144-2169)Online publication date: 10-Jun-2015
https://dl.acm.org/doi/10.1002/cpe.3418
Knijnenburg BWillemsen MGantner ZSoncu HNewell C(2012)Explaining the user experience of recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-011-9118-422:4-5(441-504)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1007/s11257-011-9118-4
García-Crespo AChamizo JRivera IMencke MColomo-Palacios RGómez-Berbís J(2009)SPETATelematics and Informatics10.1016/j.tele.2008.11.00826:3(306-315)Online publication date: 1-Aug-2009
https://dl.acm.org/doi/10.1016/j.tele.2008.11.008
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Interacting with recommender systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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CHI EA '99: CHI '99 Extended Abstracts on Human Factors in Computing Systems

May 1999

380 pages

ISBN:1581131585

DOI:10.1145/632716

Conference Chair:
Michael E. Atwood
Drexel University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 May 1999

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CHI99: Conference on Human Factors in Computing Systems

May 15 - 20, 1999

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Cited By

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Fan HHussain FHussain O(2015)Semantic client-side approach for web personalization of SaaS-based cloud servicesConcurrency and Computation: Practice & Experience10.1002/cpe.341827:8(2144-2169)Online publication date: 10-Jun-2015
https://dl.acm.org/doi/10.1002/cpe.3418
Knijnenburg BWillemsen MGantner ZSoncu HNewell C(2012)Explaining the user experience of recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-011-9118-422:4-5(441-504)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1007/s11257-011-9118-4
García-Crespo AChamizo JRivera IMencke MColomo-Palacios RGómez-Berbís J(2009)SPETATelematics and Informatics10.1016/j.tele.2008.11.00826:3(306-315)Online publication date: 1-Aug-2009
https://dl.acm.org/doi/10.1016/j.tele.2008.11.008
Hart MBotha RCilliers C(2008)Do online buying behaviour and attitudes to web personalization vary by age group?Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology10.1145/1456659.1456670(86-93)Online publication date: 6-Oct-2008
https://dl.acm.org/doi/10.1145/1456659.1456670
Cheon HLee H(2005)Opinion leader based filteringProceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences10.1007/11599517_40(352-359)Online publication date: 12-Dec-2005
https://dl.acm.org/doi/10.1007/11599517_40
Degemmis MLops PSemeraro GCostabile MGuida SLicchelli O(2004)Improving collaborative recommender systems by means of user profilesDesigning personalized user experiences in eCommerce10.5555/1011236.1011250(253-274)Online publication date: 1-Jan-2004
https://dl.acm.org/doi/10.5555/1011236.1011250
Perugini SGonçalves MFox E(2004)Recommender Systems ResearchJournal of Intelligent Information Systems10.1023/B:JIIS.0000039532.05533.9923:2(107-143)Online publication date: 1-Sep-2004
https://dl.acm.org/doi/10.1023/B%3AJIIS.0000039532.05533.99
Adomavicius GTuzhilin A(2001)Expert-Driven Validation of Rule-Based User Models in Personalization ApplicationsData Mining and Knowledge Discovery10.1023/A:10098398276835:1-2(33-58)Online publication date: 1-Jan-2001
https://dl.acm.org/doi/10.1023/A%3A1009839827683
Adomavicius GTuzhilin A(2001)Multidimensional Recommender Systems: A Data Warehousing ApproachElectronic Commerce10.1007/3-540-45598-1_17(180-192)Online publication date: 29-Oct-2001
https://doi.org/10.1007/3-540-45598-1_17
Linton FSchaefer H(2000)Recommender Systems for LearningUser Modeling and User-Adapted Interaction10.1023/A:102652193119410:2-3(181-208)Online publication date: 4-Feb-2000
https://dl.acm.org/doi/10.1023/A%3A1026521931194

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A time-based approach to effective recommender systems using implicit feedback

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