Article

Making recommendations better: an analytic model for human-recommender interaction

Authors:

Sean M. McNee,

John Riedl,

Joseph A. KonstanAuthors Info & Claims

CHI EA '06: CHI '06 Extended Abstracts on Human Factors in Computing Systems

Pages 1103 - 1108

https://doi.org/10.1145/1125451.1125660

Published: 21 April 2006 Publication History

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Abstract

Recommender systems do not always generate good recommendations for users. In order to improve recommender quality, we argue that recommenders need a deeper understanding of users and their information seeking tasks. Human-Recommender Interaction (HRI) provides a framework and a methodology for understanding users, their tasks, and recommender algorithms using a common language. Further, by using an analytic process model, HRI becomes not only descriptive, but also constructive. It can help with the design and structure of a recommender system, and it can act as a bridge between user information seeking tasks and recommender algorithms.

References

[1]

Case, D.O. Looking for Information: A Survey of Research on Information Seeking, Needs, and Behavior. Academic Press: San Diego, CA, 2002.

Google Scholar

[2]

Hackos, J.T., Redish, J.C. User and Task Analysis for Interface Design. Wiley: Hoboken, NJ, 1998.

Digital Library

Google Scholar

[3]

Herlocker, J.L., Konstan, J.A., Terveen, L.G., and Riedl, J.T. Evaluating Collaborative Filtering Recommender Systems. ACM TOIS 22, 1 (2004), 5--53.

Digital Library

Google Scholar

[4]

Im, I., Hars, A. Finding Information Just For You: Knowledge Reuse Using Collaborative Filtering Systems. In Proc. of ICIS 2001, Association for Information Systems (2001), 349--360.

Google Scholar

[5]

McNee, S.M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S.K., Rashid, A.M., Konstan, J.A. and Riedl, J. On the Recommending of Citations for Research Papers. In Proc. of CSCW 2002, ACM Press (2002), 116--125.

Digital Library

Google Scholar

[6]

Torres, R., McNee, S.M., Abel, M., Konstan, J.A., and Riedl, J. Enhancing digital libraries with TechLens+. In Proc. of JCDL 2004, ACM Press (2004) 228--236.

Digital Library

Google Scholar

[7]

Zaslow, J. "If TiVo Thinks You Are Gay, Here's How To Set It Straight -- Amazon.com Knows You, Too, Based on What You Buy; Why All the Cartoons?" The Wall Street Journal, sect. A, p. 1, November 26, 2002.

Google Scholar

[8]

Ziegler, C.N., McNee, S.M., Konstan, J.A., and Lausen, G., Improving Recommendation Lists through Topic Diversification. In Proc. of WWW 2005, ACM Press (2005), 22--32.

Digital Library

Google Scholar

Cited By

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Binst B(2024)How to Evaluate Serendipity in Recommender Systems: the Need for a SerendiptionnaireProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688017(1335-1341)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688017
Hasanah AKusumo D(2024)User-Centric Evaluation of Novelty and Explanation Aspects of Recommender Systems in an Indonesia E-commerce Platform Based on Perceived Usefulness2024 2nd International Conference on Software Engineering and Information Technology (ICoSEIT)10.1109/ICoSEIT60086.2024.10497523(70-75)Online publication date: 28-Feb-2024
https://doi.org/10.1109/ICoSEIT60086.2024.10497523
Dodeja LTambwekar PHedlund-Botti EGombolay M(2024)Towards the design of user-centric strategy recommendation systems for collaborative Human–AI tasksInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103216184(103216)Online publication date: Apr-2024
https://doi.org/10.1016/j.ijhcs.2023.103216
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Index Terms

Making recommendations better: an analytic model for human-recommender interaction
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

CHI EA '06: CHI '06 Extended Abstracts on Human Factors in Computing Systems

April 2006

1914 pages

ISBN:1595932984

DOI:10.1145/1125451

Conference Chair:
Gary Olson
University of Michigan, Ann Arbor, MI
,
Program Chair:
Robin Jeffries
Google, USA

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2006

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CHI06: CHI 2006 Conference on Human Factors in Computing Systems

April 22 - 27, 2006

Québec, Montréal, Canada

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Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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Binst B(2024)How to Evaluate Serendipity in Recommender Systems: the Need for a SerendiptionnaireProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688017(1335-1341)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688017
Hasanah AKusumo D(2024)User-Centric Evaluation of Novelty and Explanation Aspects of Recommender Systems in an Indonesia E-commerce Platform Based on Perceived Usefulness2024 2nd International Conference on Software Engineering and Information Technology (ICoSEIT)10.1109/ICoSEIT60086.2024.10497523(70-75)Online publication date: 28-Feb-2024
https://doi.org/10.1109/ICoSEIT60086.2024.10497523
Dodeja LTambwekar PHedlund-Botti EGombolay M(2024)Towards the design of user-centric strategy recommendation systems for collaborative Human–AI tasksInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103216184(103216)Online publication date: Apr-2024
https://doi.org/10.1016/j.ijhcs.2023.103216
Wang JKim JKim H(2024)Purchase intention in TikTok streaming commerce: the role of recommendation accuracy, streamer’s attractiveness, and consumer-to-consumer interactionsReview of Managerial Science10.1007/s11846-024-00810-9Online publication date: 1-Oct-2024
https://doi.org/10.1007/s11846-024-00810-9
Torkamaan HTahaei MBuijsman SXiao ZWilkinson DKnijnenburg B(2024)The Role of Human-Centered AI in User Modeling, Adaptation, and Personalization—Models, Frameworks, and ParadigmsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_2(43-84)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_2
Sun RAkella AKong RZhou MKonstan J(2023)Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field ExperimentInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226279640:22(7233-7247)Online publication date: 5-Oct-2023
https://doi.org/10.1080/10447318.2023.2262796
Liang SAlimu NSi HLi HMi C(2023)Influence of Artificial Intelligence Recommendation on Consumers’ Purchase Intention Under the Information Cocoon EffectHCI in Business, Government and Organizations10.1007/978-3-031-35969-9_17(249-259)Online publication date: 17-Jul-2023
https://doi.org/10.1007/978-3-031-35969-9_17
Zangerle EBauer C(2022)Evaluating Recommender Systems: Survey and FrameworkACM Computing Surveys10.1145/355653655:8(1-38)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3556536
Wang YSharma MXu CBadam SSun QRichardson LChung LChi EChen MZhang ARangwala H(2022)Surrogate for Long-Term User Experience in Recommender SystemsProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539073(4100-4109)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539073
Lee DSetlur VTory MKarahalios KParameswaran A(2022)Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative StudyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.308575128:12(4225-4239)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3085751
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Recommendations

Integrating collaborative filtering and matching-based search for product recommendations

New Recommendation Techniques for Multicriteria Rating Systems

Naïve filterbots for robust cold-start recommendations