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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

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
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Index Terms

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

Recommendations

User Evaluations on Sentiment-based Recommendation Explanations

The explanation interface has been recognized as important in recommender systems because it can allow users to better judge the relevance of recommendations to their preferences and, hence, make more informed decisions. In different product domains, ...
Justifying Recommendations through Aspect-based Sentiment Analysis of Users Reviews
UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

In this paper we present a methodology to justify the suggestions generated by a recommendation algorithm through the identification of relevant and distinguishing characteristics of the recommended item, automatically extracted by mining users' ...
Expert Recommendations Based on Opinion Mining of User-Generated Product Reviews

In this article, we introduce the idea of expert recommendations whose objective is to relate review comments with users' tasks or expectations. We propose to use fine-grained information such as opinions and suggestions extracted using natural language ...

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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.

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: 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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IUI '25

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30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

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Cited By

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
Balog KRadlinski FPetrov A(2023)Measuring the Impact of Explanation Bias: A Study of Natural Language Justifications for Recommender SystemsExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3585748(1-8)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544549.3585748
Filippo Hu ZMauro NArdissono L(2023)Image-Based Information Filtering to Compare and Select Items2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00007(1-8)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00007
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