research-article

TriRank: Review-aware Explainable Recommendation by Modeling Aspects

Authors:

Xiao ChenAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 1661 - 1670

https://doi.org/10.1145/2806416.2806504

Published: 17 October 2015 Publication History

Abstract

Most existing collaborative filtering techniques have focused on modeling the binary relation of users to items by extracting from user ratings. Aside from users' ratings, their affiliated reviews often provide the rationale for their ratings and identify what aspects of the item they cared most about. We explore the rich evidence source of aspects in user reviews to improve top-N recommendation. By extracting aspects (i.e., the specific properties of items) from textual reviews, we enrich the user--item binary relation to a user--item--aspect ternary relation. We model the ternary relation as a heterogeneous tripartite graph, casting the recommendation task as one of vertex ranking. We devise a generic algorithm for ranking on tripartite graphs -- TriRank -- and specialize it for personalized recommendation. Experiments on two public review datasets show that it consistently outperforms state-of-the-art methods. Most importantly, TriRank endows the recommender system with a higher degree of explainability and transparency by modeling aspects in reviews. It allows users to interact with the system through their aspect preferences, assisting users in making informed decisions.

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Yi QWu LTang JZeng YSong Z(2025)Hybrid contrastive multi-scenario learning for multi-task sequential-dependence recommendationNeural Networks10.1016/j.neunet.2024.106953183(106953)Online publication date: Mar-2025
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Gunathilaka TManage PZhang JLi YKelly W(2025)Addressing sparse data challenges in recommendation systems: A systematic review of rating estimation using sparse rating data and profile enrichment techniquesIntelligent Systems with Applications10.1016/j.iswa.2024.20047425(200474)Online publication date: Mar-2025
https://doi.org/10.1016/j.iswa.2024.200474
Zhao YXu YLiu YYang LJiang WNing WSun XCui L(2025)Conditional Potential User Mining framework via explainable surrogate modelsExpert Systems with Applications10.1016/j.eswa.2024.125587262(125587)Online publication date: Mar-2025
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Index Terms

TriRank: Review-aware Explainable Recommendation by Modeling Aspects
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Conferences

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Copyright © 2015 ACM.

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Published: 17 October 2015

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CIKM'15

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

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Cited By

Yi QWu LTang JZeng YSong Z(2025)Hybrid contrastive multi-scenario learning for multi-task sequential-dependence recommendationNeural Networks10.1016/j.neunet.2024.106953183(106953)Online publication date: Mar-2025
https://doi.org/10.1016/j.neunet.2024.106953
Gunathilaka TManage PZhang JLi YKelly W(2025)Addressing sparse data challenges in recommendation systems: A systematic review of rating estimation using sparse rating data and profile enrichment techniquesIntelligent Systems with Applications10.1016/j.iswa.2024.20047425(200474)Online publication date: Mar-2025
https://doi.org/10.1016/j.iswa.2024.200474
Zhao YXu YLiu YYang LJiang WNing WSun XCui L(2025)Conditional Potential User Mining framework via explainable surrogate modelsExpert Systems with Applications10.1016/j.eswa.2024.125587262(125587)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125587
Le TLauw H(2025)Learning to rank aspects and opinions for comparative explanationsMachine Learning10.1007/s10994-024-06699-5114:1Online publication date: 16-Jan-2025
https://doi.org/10.1007/s10994-024-06699-5
Cai WYang MLin L(2024)An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group PreferencesSystems10.3390/systems1211049112:11(491)Online publication date: 14-Nov-2024
https://doi.org/10.3390/systems12110491
Cui YZhou PYu HSun PCao HYang P(2024)ASKAT: Aspect Sentiment Knowledge Graph Attention Network for RecommendationElectronics10.3390/electronics1301021613:1(216)Online publication date: 3-Jan-2024
https://doi.org/10.3390/electronics13010216
Zhu XXia XWu YZhao W(2024)Enhancing Explainable Recommendations: Integrating Reason Generation and Rating Prediction through Multi-Task LearningApplied Sciences10.3390/app1418830314:18(8303)Online publication date: 14-Sep-2024
https://doi.org/10.3390/app14188303
郭泽(2024)Recommendation Model of Graph Convolutional Network Based on Multi-SubgraphComputer Science and Application10.12677/csa.2024.14715714:07(1-9)Online publication date: 2024
https://doi.org/10.12677/csa.2024.147157
Chen CZhang YLi YWang JQi LXu XZheng XYin J(2024)Post-Training Attribute Unlearning in Recommender SystemsACM Transactions on Information Systems10.1145/370198743:1(1-28)Online publication date: 6-Nov-2024
https://dl.acm.org/doi/10.1145/3701987
Le TLauw H(2024)Question-Attentive Review-Level Explanation for Neural Rating RegressionACM Transactions on Intelligent Systems and Technology10.1145/369951615:6(1-25)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3699516
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