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Explaining Compound Critiques

Authors:

Kevin McCarthy,

Lorraine McGinty,

Barry SmythAuthors Info & Claims

Artificial Intelligence Review, Volume 24, Issue 2

Pages 199 - 220

https://doi.org/10.1007/s10462-005-4614-8

Published: 01 October 2005 Publication History

Abstract

When it comes to buying expensive goods people expect to be skillfully steered through the options by well-informed sales assistants who are capable of balancing the user's many and varied requirements. In addition users often need to be educated about the product space, especially if they are to come to understand what is available and why certain options are being recommended by the sales-assistant. It is now well accepted that interactive recommender systems, the on-line equivalent of a sales assistant, also need to educate users about the product space and to justify their recommendations. In this paper we focus on a novel approach to explanation. Instead of attempting to justify a particular recommendation we focus on how certain types of feedback can help users to understand the recommendation opportunities that remain if the current recommendation should not meet their requirements. Specifically, we describe how this approach to explanation is tightly coupled with the generation of compound critiques, which act as a form of feedback for users. Furthermore, we argue that these explanation-rich critiques have the potential to dramatically improve recommender performance and usability.

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Cited By

Yang HSanner SWu GZhou J(2021)Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive RecommendationProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456814(55-64)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450613.3456814
Li HSanner SLuo KWu G(2020)A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender SystemsProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412240(13-22)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1145/3383313.3412240
Luo KSanner SWu GLi HYang H(2020)Latent Linear Critiquing for Conversational Recommender SystemsProceedings of The Web Conference 202010.1145/3366423.3380003(2535-2541)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380003
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Index Terms

Explaining Compound Critiques
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
    2. Philosophical/theoretical foundations of artificial intelligence
2. Theory of computation
  1. Logic
  2. Semantics and reasoning
    1. Program reasoning

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Published In

cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 24, Issue 2

October 2005

119 pages

ISSN:0269-2821

Issue’s Table of Contents

Copyright © Copyright © 2005 Springer.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 October 2005

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Cited By

Yang HSanner SWu GZhou J(2021)Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive RecommendationProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456814(55-64)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450613.3456814
Li HSanner SLuo KWu G(2020)A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender SystemsProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412240(13-22)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1145/3383313.3412240
Luo KSanner SWu GLi HYang H(2020)Latent Linear Critiquing for Conversational Recommender SystemsProceedings of The Web Conference 202010.1145/3366423.3380003(2535-2541)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380003
Wu GLuo KSanner SSoh HBogers TSaid ABrusilovsky PTikk D(2019)Deep language-based critiquing for recommender systemsProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347009(137-145)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347009
Chen LYan DWang F(2019)User Evaluations on Sentiment-based Recommendation ExplanationsACM Transactions on Interactive Intelligent Systems10.1145/32828789:4(1-38)Online publication date: 9-Aug-2019
https://dl.acm.org/doi/10.1145/3282878
Chen LWang FPapadopoulos GKuflik TChen FDuarte CFu W(2017)Explaining Recommendations Based on Feature Sentiments in Product ReviewsProceedings of the 22nd International Conference on Intelligent User Interfaces10.1145/3025171.3025173(17-28)Online publication date: 7-Mar-2017
https://dl.acm.org/doi/10.1145/3025171.3025173
Nunes IJannach D(2017)A systematic review and taxonomy of explanations in decision support and recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-017-9195-027:3-5(393-444)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11257-017-9195-0
Muhammad KLawlor ASmyth BNichols JMahmud JO'Donovan JConati CZancanaro M(2016)A Live-User Study of Opinionated Explanations for Recommender SystemsProceedings of the 21st International Conference on Intelligent User Interfaces10.1145/2856767.2856813(256-260)Online publication date: 7-Mar-2016
https://dl.acm.org/doi/10.1145/2856767.2856813
Grasch PFelfernig AReinfrank FYang QKing ILi QPu PKarypis G(2013)ReCommentProceedings of the 7th ACM conference on Recommender systems10.1145/2507157.2507161(157-164)Online publication date: 12-Oct-2013
https://dl.acm.org/doi/10.1145/2507157.2507161
Chen LPu P(2009)Interaction design guidelines on critiquing-based recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-008-9057-x19:3(167-206)Online publication date: 1-Aug-2009
https://dl.acm.org/doi/10.1007/s11257-008-9057-x
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