Article

Explanations of recommendations

Author:

Nava TintarevAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 203 - 206

https://doi.org/10.1145/1297231.1297275

Published: 19 October 2007 Publication History

Abstract

This thesis focuses on explanations of recommendations. Explanations can have many advantages, from inspiring user trust to helping users make good decisions. We have identified seven different aims of explanations, and in this thesis we will consider how explanations can be optimized for some of these aims. We will consider both an explanation's content and its presentation. As a domain, we are currently investigating explanations for a movie recommender, and developing a prototype system. This paper summarizes the goals of the thesis, the methodology we are using, the work done so far and our intended future work.

References

[1]

Bilgic, M. and Mooney, R. J. Explaining recommendations: Satisfaction vs. promotion. Beyond Personalization Workshop, IUI, 2005.

[2]

Buchanan, B. G. & Shortliffe, E. H. (ed.) The Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project 30-35 Addison-Wesley Publishing Company, 571--665, 1985

Digital Library

[3]

Carenini, G. and Moore, J. An empirical study of the influence of argument conciseness on argument effectiveness. ACL, 2000.

Digital Library

[4]

Chen, L. and Pu, P. Trust building in recommender agents. WPRSIUI workshop, 2002.

[5]

Czarkowski, M. A Scrutable Adaptive Hypertext. PhD thesis, University of Sydney, 2006.

[6]

Herlocker, J. L., Konstan, J. A. and Riedl, J. Explaining collaborative filtering recommendations. In CSCW, 2000.

Digital Library

[7]

Hingston, M. User friendly recommender systems. Honours thesis, University of Sydney, 2006.

[8]

Hong, T. Contributing Factors to the Use of Health-Related Websites. Journal of Health Communication, 11:2, 149--165, 2006

[9]

Law, A. S., Freer, Y. Hunter, J., Logie, R.; N., N. M. & Quinn, J. A comparison of graphical and textual presentations of time series data to support medical decision making in the neonatal intensive care unit. J Clin Monit Comput., 19:3, 183--94, 2005

[10]

Kincaid, J. P., Fishburne Jr., R. P., Rogers, R. L. and Chissom, B. S. Derivation of new readability formulas. Technical report, TN: Naval Technical Training, U. S. Naval Air Station, Memphis, TN, 1975.

[11]

Kilgarriff, A., and Tugwell, D. (2001). Word sketch: Extraction and display of significant collocations for lexicography. Collocations Workshop in association with ACL, 2001.

[12]

Lin, D. An information-theoretic definition of similarity. Proceedings of the Fifteenth International Conference on Machine Learning, 296--304, 1998.

Digital Library

[13]

McCarthy, K., Reilly, J., McGinty, L. & Smyth, B. Thinking Positively - Explanatory Feedback for Conversational Recommender Systems. European Conference on Case-Based Reasoning Explanation Workshop, 2004

[14]

McSherry, D. Explanation in recommender systems. Artificial Intelligence Review, 24(2):179--197, 2005.

Digital Library

[15]

McNee, S. M., J. Riedl, and J. A. Konstan. Being accurate is not enough: How accuracy metrics have hurt recommender systems. Extended Abstracts, CHI 2006

Digital Library

[16]

Ohanian, R. Construction and Validation of a Scale to Measure Celebrity Endorsers' Perceived Expertise, Trustworthiness, and Attractiveness. Journal of Advertising, 19:3, 39, 1990.

[17]

Sinha, R. & Swearingen, K. The role of transparency in recommender systems. Conference on Human Factors in Computing Systems, 2002

Digital Library

[18]

Tintarev, N. & Masthoff, J. Similarity for News Recommender Systems. WPRSIUI, in association with AH, 2006.

[19]

Tintarev, N. and Masthoff, J. Survey of explanations in recommender systems. WPRSIUI, in association with ICDE, 2007.

Digital Library

[20]

Tintarev, N. & Masthoff, J. Effective Explanations of Recommendations: User-Centered Design. In ACM Recommender System, 2007.

Digital Library

[21]

Ziegler, C., McNee, S. M., Konstan, J. A. and Lausen, G. Improving recommendation lists through topic diversification. WWW 2005

Digital Library

Cited By

Schott KPapenmeier AHienert DKern D(2024)What Did I Say Again? Relating User Needs to Search Outcomes in Conversational CommerceProceedings of Mensch und Computer 202410.1145/3670653.3670680(129-139)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1145/3670653.3670680
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Tran TPolat Erdeniz SFelfernig ALubos SEl Mansi MLe V(2024)Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691708(1108-1112)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691708
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Index Terms

Explanations of recommendations
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
  2. Interaction design
    1. Interaction design process and methods
      1. User centered design

Recommendations

Explaining collaborative filtering recommendations
CSCW '00: Proceedings of the 2000 ACM conference on Computer supported cooperative work

Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. ...
LLM-generated Explanations for Recommender Systems
UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

Users are often confronted with situations where they have to decide in favor or against an offered item, like a book, movie, or recipe. Those suggested items are commonly determined by a recommender system, which considers personal preferences to ...
User Needs for Explanations of Recommendations: In-depth Analyses of the Role of Item Domain and Personal Characteristics
UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

Explanations can be provided with different goals, such as clarifying how the system works, how well the recommended item meets the user’s preferences, and how an explanation helps the user select an item faster. Although extensive research has been ...

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Information & Contributors

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Published In

cover image ACM Conferences

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

Copyright © 2007 ACM.

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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Publication History

Published: 19 October 2007

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RecSys07

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Schott KPapenmeier AHienert DKern D(2024)What Did I Say Again? Relating User Needs to Search Outcomes in Conversational CommerceProceedings of Mensch und Computer 202410.1145/3670653.3670680(129-139)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1145/3670653.3670680
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Tran TPolat Erdeniz SFelfernig ALubos SEl Mansi MLe V(2024)Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691708(1108-1112)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691708
Szymanski MOoge JDe Croon RVanden Abeele VVerbert K(2024)Feedback, Control, or Explanations? Supporting Teachers With Steerable Distractor-Generating AIProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636933(690-700)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636933
Zhou XMiao C(2024)Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation With InterpretabilityIEEE Transactions on Multimedia10.1109/TMM.2024.336987526(7543-7554)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3369875
Kleemann TLoepp BZiegler J(2024)Knowledge Graph-Based Integration of Conversational Advisors and Faceted FilteringInteracting with Computers10.1093/iwc/iwae044Online publication date: 18-Sep-2024
https://doi.org/10.1093/iwc/iwae044
Mentec FMiklos ZHervieu SRoger T(2024)AI4HR RecruiterProcedia Computer Science10.1016/j.procs.2023.10.111225:C(1231-1240)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.10.111
Lei DHe YZeng J(2024)What Is the Focus of XAI in UI Design? Prioritizing UI Design Principles for Enhancing XAI User ExperienceArtificial Intelligence in HCI10.1007/978-3-031-60606-9_13(219-237)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-60606-9_13
Sacharidis D(2024)Fairness and Explainability for Enabling Trust in AI SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_3(85-110)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_3
Wang LCai Zde Melo GCao ZHe LWilliams BChen YNeville J(2023)Disentangled CVAEs with contrastive learning for explainable recommendationProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i11.26604(13691-13699)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i11.26604
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