Explainable Recommendation

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Explainable recommendation studies how to make personalized suggestions understandable, useful, and open to scrutiny, from movies and music to jobs and healthcare. Approaches include interpretable rules and knowledge-graph paths, feature attribution and counterfactuals, and natural-language explanations generated from reviews, user profiles, or structured evidence. Evaluation goes beyond ranking accuracy to test faithfulness, factual grounding, preference alignment, user trust, and effects on choices, using benchmarks as well as user studies and online experiments. A central challenge is ensuring that explanations genuinely reflect and influence the recommendation process while remaining reliable, personalized, and efficient at scale.

Latest papers

41 papers8 papers in the last four weeks, up 60% on the four weeks before. 0.0% of all new papers.

TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation
Apr 10, 2026Qingzhuo Wang, Leilei Wen, Juntao Chen +4Sequential Recommendation Recommender Systems
Tongji University
A Logical-Rule Autoencoder for Interpretable Recommendations
Apr 5, 2026Jinhao Pan, Bowen Wei, Ziwei ZhuExplainable Recommendation Collaborative Filtering
Department of Computer Science, George Mason University, Fairfax, Virginia, USA
Synapse: Evolving Job-Person Fit with Explainable Two-phase Retrieval and LLM-guided Genetic Resume Optimization
Apr 2, 2026Ansel Kaplan Erol, Seohee Yoon, Keenan Hom +1Job-Candidate Matching Recommender Systems
Georgia Institute of Technology, Atlanta, GA, USA
AI-Mediated Explainable Regulation for Justice
Mar 31, 2026Thomas Hofweber, Andreas Sudmann, Evangelos PournarasAI-Assisted Decision Making Explainable Recommendation
University North Carolina at Chapel Hill · University of Bonn · University of Leeds
Visual or Textual: Effects of Explanation Format and Personal Characteristics on the Perception of Explanations in an Educational Recommender System
Mar 26, 2026Qurat Ul Ain, Mohamed Amine Chatti, Nasim Yazdian Varjani +2Explainable Recommendation Explanation Evaluation
University of Duisburg-Essen Duisburg, Germany · RWTH Aachen University Aachen, Germany
RecBundle: A Next-Generation Geometric Paradigm for Explainable Recommender Systems
Mar 17, 2026Hui Wang, Tianzhu Hu, Mingming Li +6Recommender Systems Geometric Representation Learning
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · JD.com, Beijing, China · School of Cyberspace Security, University of Chinese Academy of Sciences, Beijing, China
JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI
Mar 15, 2026Mayank Vyas, Abhijit Chakraborty, Vivek GuptaJob-Candidate Matching Hybrid IR
Arizona State University
Beyond Factual Correctness: Mitigating Preference-Inconsistent Explanations in Explainable Recommendation
Mar 3, 2026Chengkai Wang, Baisong LiuExplainable Recommendation Large Language Model-Based Recommendation
Ningbo University, Ningbo, China
Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing
Feb 25, 2026Deogyong Kim, Junseong Lee, Jeongeun Lee +4Recommender Systems Large Language Model-Based Recommendation
Yonsei University, Seoul, South Korea · NAVER, Seongnam, South Korea
DReX: An Explainable Deep Learning-based Multimodal Recommendation Framework
Feb 23, 2026Adamya Shyam, Venkateswara Rao Kagita, Bharti Rana +1Gated Recurrent Unit Multimodal Recommendation
University of Delhi, Delhi, India · National Institute of Technology, Warangal, India
Exploration Space Theory: Formal Foundations for Prerequisite-Aware Location-Based Recommendation
Feb 21, 2026Madjid SadallahRecommender Systems Explainable Recommendation
LIRIS, Université Claude Bernard Lyon 1, CNRS, 43 boulevard du 11 novembre 1918, 69622 Villeurbanne, France

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TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation

Qingzhuo Wang, Leilei Wen, Juntao Chen, Kunyu Peng+3

Tongji University

Sequential Recommendation Recommender Systems User Preference Modeling Explainable Recommendation

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In this paper, we propose a sequential recommendation model that integrates Time-aware personalization, Multi-interest personalization, and Explanation personalization for Personalized Sequential Recommendation (TME-PSR). That is, we consider the differences across different users in temporal rhythm preference, multiple fine-grained latent interests, and the personalized semantic alignment between recommendations and explanations. Specifically, the proposed TME-PSR model employs a dual-view gated time encoder to capture personalized temporal rhythms, a lightweight multihead Linear Recurrent Unit architecture that enables fine-grained sub-interest modeling with improved efficiency, and a dynamic dual-branch mutual information weighting mechanism to achieve personalized alignment between recommendations and explanations. Extensive experiments on real-world datasets demonstrate that our method consistently improves recommendation accuracy and explanation quality, at a lower computational cost.