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Explaining Recommendations Based on Feature Sentiments in Product Reviews

Authors:

Feng WangAuthors Info & Claims

IUI '17: Proceedings of the 22nd International Conference on Intelligent User Interfaces

Pages 17 - 28

https://doi.org/10.1145/3025171.3025173

Published: 07 March 2017 Publication History

Abstract

The explanation interface has been recognized important in recommender systems as it can help users evaluate recommendations in a more informed way for deciding which ones are relevant to their interests. In different decision environments, the specific aim of explanation can be different. In high-investment product domains (e.g., digital cameras, laptops) for which users usually attempt to avoid financial risk, how to support users to construct stable preferences and make better decisions is particularly crucial. In this paper, we propose a novel explanation interface that emphasizes explaining the tradeoff properties within a set of recommendations in terms of both their static specifications and feature sentiments extracted from product reviews. The objective is to assist users in more effectively exploring and understanding product space, and being able to better formulate their preferences for products by learning from other customers' experiences. Through two user studies (in form of both before-after and within-subjects experiments), we empirically identify the practical role of feature sentiments in combination with static specifications in producing tradeoff-oriented explanations. Specifically, we find that our explanation interface can be more effective to increase users' product knowledge, preference certainty, perceived information usefulness, recommendation transparency and quality, and purchase intention.

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Cited By

Le TLauw H(2025)Learning to rank aspects and opinions for comparative explanationsMachine Language10.1007/s10994-024-06699-5114:1Online publication date: 16-Jan-2025
https://dl.acm.org/doi/10.1007/s10994-024-06699-5
Liao HWang SCheng HZhang WZhang JZhou MLu KMao RXie X(2024)Aspect-Enhanced Explainable Recommendation with Multi-modal Contrastive LearningACM Transactions on Intelligent Systems and Technology10.1145/367323416:1(1-24)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3673234
Kotkov DMedlar ALiu YGlowacka DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)On the Negative Perception of Cross-domain Recommendations and ExplanationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657735(2102-2113)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657735
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Index Terms

Explaining Recommendations Based on Feature Sentiments in Product Reviews
1. Human-centered computing

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Published In

cover image ACM Conferences

IUI '17: Proceedings of the 22nd International Conference on Intelligent User Interfaces

March 2017

654 pages

ISBN:9781450343480

DOI:10.1145/3025171

General Chairs:
George A. Papadopoulos
The University of Cyprus, Cyprus
,
Tsvi Kuflik
The University of Haifa, Israel
,
Program Chairs:
Fang Chen
Data61, CSIRO Australia
,
Carlos Duarte
The University of Lisbon, Portugal
,
Wai-Tat Fu
The University of Illinois at Urbana-Champaign, USA

Copyright © 2017 ACM.

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Published: 07 March 2017

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Hong Kong Research Grants Council

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IUI'17

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IUI'17: 22nd International Conference on Intelligent User Interfaces

March 13 - 16, 2017

Limassol, Cyprus

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IUI '17 Paper Acceptance Rate 63 of 272 submissions, 23%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Le TLauw H(2025)Learning to rank aspects and opinions for comparative explanationsMachine Language10.1007/s10994-024-06699-5114:1Online publication date: 16-Jan-2025
https://dl.acm.org/doi/10.1007/s10994-024-06699-5
Liao HWang SCheng HZhang WZhang JZhou MLu KMao RXie X(2024)Aspect-Enhanced Explainable Recommendation with Multi-modal Contrastive LearningACM Transactions on Intelligent Systems and Technology10.1145/367323416:1(1-24)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3673234
Kotkov DMedlar ALiu YGlowacka DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)On the Negative Perception of Cross-domain Recommendations and ExplanationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657735(2102-2113)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657735
Fok RLipka NSun TSiu A(2024)Marco: Supporting Business Document Workflows via Collection-Centric Information Foraging with Large Language ModelsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641969(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641969
Yacouba KOuedraogo TKaboré K(2024)The Personalization of Justified Recommendations Using the Users Profile Interest and ReviewsIntelligent Informatics10.1007/978-981-97-2147-4_12(159-175)Online publication date: 18-Oct-2024
https://doi.org/10.1007/978-981-97-2147-4_12
Jin YChen LCai WZhao X(2023)CRS-Que: A User-centric Evaluation Framework for Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/36315342:1(1-34)Online publication date: 2-Nov-2023
https://dl.acm.org/doi/10.1145/3631534
Brachman MPan QDo HDugan CChaudhary AJohnson JRai PChakraborti TGschwind TLaredo JMiksovic CScotton PTalamadupula KThomas G(2023)Follow the Successful Herd: Towards Explanations for Improved Use and Mental Models of Natural Language SystemsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584088(220-239)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584088
Li LZhang YChen L(2023)Personalized Prompt Learning for Explainable RecommendationACM Transactions on Information Systems10.1145/358048841:4(1-26)Online publication date: 23-Mar-2023
https://dl.acm.org/doi/10.1145/3580488
Hu ZMauro NPetrone GArdissono L(2023)Service-based Presentation of Multimodal Information for the Justification of Recommender Systems ResultsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592962(46-53)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592962
Yan DChen L(2023)The Influence of Personality Traits on User Interaction with Recommendation InterfacesACM Transactions on Interactive Intelligent Systems10.1145/355877213:1(1-39)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.1145/3558772
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