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research-article

Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems

Authors:

Georgina Peake,

Jun WangAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2060 - 2069

https://doi.org/10.1145/3219819.3220072

Published: 19 July 2018 Publication History

Abstract

The widescale use of machine learning algorithms to drive decision-making has highlighted the critical importance of ensuring the interpretability of such models in order to engender trust in their output. The state-of-the-art recommendation systems use black-box latent factor models that provide no explanation of why a recommendation has been made, as they abstract their decision processes to a high-dimensional latent space which is beyond the direct comprehension of humans. We propose a novel approach for extracting explanations from latent factor recommendation systems by training association rules on the output of a matrix factorisation black-box model. By taking advantage of the interpretable structure of association rules, we demonstrate that predictive accuracy of the recommendation model can be maintained whilst yielding explanations with high fidelity to the black-box model on a unique industry dataset. Our approach mitigates the accuracy-interpretability trade-off whilst avoiding the need to sacrifice flexibility or use external data sources. We also contribute to the ill-defined problem of evaluating interpretability.

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Cited By

Podo LPrenkaj BVelardi P(2025)Agnostic Visual Recommendation Systems: Open Challenges and Future DirectionsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.337457131:3(1902-1917)Online publication date: Mar-2025
https://doi.org/10.1109/TVCG.2024.3374571
Wang SXie BDing LChen JXiang Y(2025)Reinforced logical reasoning over KGs for interpretable recommendation systemMachine Learning10.1007/s10994-024-06646-4114:4Online publication date: 19-Feb-2025
https://doi.org/10.1007/s10994-024-06646-4
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
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Index Terms

Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Learning from implicit feedback
    2. Machine learning approaches
      1. Learning latent representations
      2. Rule learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

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Published: 19 July 2018

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Podo LPrenkaj BVelardi P(2025)Agnostic Visual Recommendation Systems: Open Challenges and Future DirectionsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.337457131:3(1902-1917)Online publication date: Mar-2025
https://doi.org/10.1109/TVCG.2024.3374571
Wang SXie BDing LChen JXiang Y(2025)Reinforced logical reasoning over KGs for interpretable recommendation systemMachine Learning10.1007/s10994-024-06646-4114:4Online publication date: 19-Feb-2025
https://doi.org/10.1007/s10994-024-06646-4
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
Li JRen YSanderson MDeng K(2024)Explaining Recommendation Fairness from a User/Item PerspectiveACM Transactions on Information Systems10.1145/369887743:1(1-30)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3698877
Möller LPadó S(2024)Explaining Neural News Recommendation with Attributions onto Reading HistoriesACM Transactions on Intelligent Systems and Technology10.1145/367323316:1(1-25)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3673233
Li HGu JLu XShen DLiu YDeng YShi GXiong H(2024)Beyond Relevance: Factor-level Causal Explanation for User Travel Decisions with Counterfactual Data AugmentationACM Transactions on Information Systems10.1145/365367342:5(1-31)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3653673
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Baklanov M(2024)CEERS: Counterfactual Evaluations of Explanations in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688015(1323-1329)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688015
Wardatzky K(2024)Evaluating the Pros and Cons of Recommender Systems ExplanationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688011(1302-1307)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688011
Lubos STran TFelfernig APolat Erdeniz SLe VBoratto LGena CMarras MGermanakos PPopescus E(2024)LLM-generated Explanations for Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665185(276-285)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665185
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