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.

The Vocabulary Is Already Here: Adapting Pretrained Interpretable Features for Generative Recommendation
Oct 27, 2026Ziluowen Luo, Ruochen Liu, Jun Yin +6Generative Recommendation Semantic ID Recommendation
Central South University Changsha, Hunan, China · The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China · Hong Kong Polytechnic University Hongkong, China +4
MAREval: A Multi-Agent Framework for Evaluating Natural Language Recommendation Explanations
Oct 26, 2026Reza Yousefi Maragheh, Jayesh Uddhav Kudase, Aysenur Inan +7Automated Evaluation LLM-as-a-Judge
Walmart Global Tech, Sunnyvale, CA, USA
Counterfactual Profile Perturbations for Recommendation Explanations: A Reproducibility Study
Oct 26, 2026Meysam Varasteh, Elizabeth McKinnie, Robin BurkeExplainable Recommendation Counterfactual Explanations
Department of Computer Science, University of Colorado, Boulder, Boulder, Colorado, USA · Department of Information Science, University of Colorado, Boulder, Boulder, Colorado, USA
Discovering Topics from Linear Autoencoders
Oct 26, 2026Jutiyakhun Srisoparp, Donlapark PonnopratRecommender Systems Explainable Recommendation
Chiang Mai University, Chiang Mai, Thailand
FairFRESH: A Neuro-Symbolic Framework for Fair and Explainable Household Food Recommendation
Oct 22, 2026Kodur Sai Vinay Sathvik, Kakoli Bora, Tejaswini R. MFair Resource Allocation Social Welfare Maximization
Department of Artificial Intelligence and Machine Learning, BNM Institute of Technology
QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
Oct 16, 2026Takashi Ishida, Taro Makino, Issa SugiuraRecommender Systems AI-Assisted Decision Making
Sakana AI
xLightGCN: Explainable Graph Neural Network Based Recommendation Systems with Most Influential Peers
Oct 14, 2026Calder Johnson, Robin CohenExplainable Recommendation Recommender Systems
Computer Science, University of Waterloo, Canada
RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation
Oct 10, 2026Cong Wang, Jin Li, Shoujin Wang +3Medication Recommendation Clinical Decision Support
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences); Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, Shandong, China · University of Technology Sydney, Sydney, NSW, Australia
Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendation
Oct 8, 2026Leikun Liang, Guoshuai Wang, Xingsheng He +4Recommender Systems Large Language Model-Based Recommendation
Alibaba Group Hangzhou, China
Contrastive Learning for Aspect Representation towards Explainable Recommendation
Oct 6, 2026Emrul Hasan, Chen DingRecommender Systems Explainable Recommendation
Department of Computer Science Toronto Metropolitan University Toronto, Canada
Evidence Before Sampling: Interpretable Implicit Negative Candidate Discovery for Recommendation
Oct 6, 2026Shreya Rajpal, Sonia Sharma, Swapnil Parekh +4Recommender Systems Explainable Recommendation
Michigan State University, USA · Intuit, USA
Reasoning with Evidence, Not Merely Rationales: Verifiable Preference Proofs for LLM-Based Recommendation
Oct 2, 2026Yu Hou, Nathaniel Kang, Pengkai Wang +1Large Language Model-Based Recommendation Explainable Recommendation
School of Mathematics and Computing (Computational Science and Engineering), Yonsei University Seoul, Republic of Korea · School of Computer Science and Engineering, Kyungpook National University Daegu, Republic of Korea · Department of Electrical and Computer Engineering, Sungkyunkwan University Suwon, Republic of Korea +1
A Systematic Benchmark of Explainable Methods for Temporal Attribution in Sequential Recommendation Systems
Sep 23, 2026Akash Pandey, Kanisha Shah, Addrish Roy +5Gradient-Based Attribution Sequential Recommendation
Capital One, AI Foundations San Jose, CA, USA · Capital One, AI Foundations New York, NY, USA · Capital One, AI Foundations McLean, VA, USA +1
Explainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery
Sep 20, 2026Xiao Liu, Yanwei Song, Srivaths Ranganathan +7Music Recommendation Recommender Systems
Google LLC USA
Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles
Sep 17, 2026Noah Mamié, Laurin van den BerghRecommender Systems Large Language Model-Based Recommendation
University of Zurich Department of Informatics
Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself
Sep 12, 2026Jiashu He, Emma Yanyang Kong, JJ Tan +1Explainable Recommendation Large Language Model-Based Recommendation
University of Pennsylvania · Netflix
Beyond Ranking Accuracy: Evaluating LLM-Cited Feature Rationales for Next Basket Repurchase Recommendation
Aug 31, 2026Yanan Cao, Anay Dombe, Murali Mohana Krishna Dandu +5Sequential Recommendation LLM Evaluation
Walmart Global Tech, Sunnyvale, CA, United States
Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals
Aug 21, 2026Meysam Varasteh, Veronika Bogina, Noam Koenigstein +1Explainable Recommendation Counterfactual Explanations
Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA · Tel Aviv University, Tel Aviv, Israel · Department of Information Science, University of Colorado Boulder, Boulder, CO 80309, USA
Pairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson
Aug 19, 2026Tanay Chowdhury, Saeideh Shahrokh EsfahaniExplainable Recommendation Large Language Model-Based Recommendation
Amazon Mountain View, CA, USA
The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations
Aug 18, 2026Emma Yanyang Kong, JJ Tan, Ishan Gupta +8LLM Evaluation LLM-as-a-Judge
Netflix · Los Gatos, California, USA
HugSelect: An Explainable Multi-Criteria Decision-Support Framework for foundation-model selection
Aug 8, 2026Alireza Joonbakhsh, Arda Canser Adalı, Slinger Jansen +2Model Selection Recommender Systems
Shiraz University · Shiraz University, Shiraz, Iran · Utrecht University +3
X-KGRank: A Knowledge Graph RAG Framework for Explainable Recommendations via Pattern Mining and LLM Re-Ranking
Aug 3, 2026Meenakshi Rajpurohit, Jainish PatelGraph Retrieval-Augmented Generation LLM Reranking
Dept. of Computer Engineering San Jose State University San Jose, CA, USA
Nudging Sustainable Choices through LLM-Generated Recommendation Explanations
Jul 28, 2026Haya Halimeh, Dietmar Jannach, Oliver MüllerRecommender Systems Large Language Model-Based Recommendation
Paderborn University, Germany · University of Klagenfurt, Austria
Two Views, One Voice: Evidence-Grounded Conversational Music Recommendation
Jul 24, 2026Sungwook Yoo, Sewook YooRecommender Systems Music Recommendation
Samsung South Korea
Music Playlist Captioning at Scale with Large Language Models
Jun 21, 2026Mathieu Delcluze, Léa Briand, Benjamin Chapus +2Music Recommendation Text Generation
Deezer Research · SJTU Paris Elite Institute of Technology
Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
May 27, 2026Weizhi Zhang, Wooseong Yang, Yuxin Cui +9Recommender Systems Large Language Model-Based Recommendation
University of Illinois Chicago, Chicago, USA · Meta, Menlo Park, USA
Developing an Intelligent Job Recommendation System Using Semantic Retrieval and Explainable AI Techniques
May 26, 2026Hussein Al Awad, Khaled Fathi OmarSemantic Search Recommender Systems
∗Master of Web Science, Syrian Virtual University, Damascus, Syria
Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies
May 21, 2026Junyu Yan, Damian Machlanski, Kurt Butler +4Feature Interaction Modeling Explainable Artificial Intelligence
School of Engineering, University of Edinburgh, Edinburgh, UK · Causality in Healthcare AI Hub (CHAI), UK · Advanced Care Research Centre, Usher School of Population Health Sciences, University of Edinburgh, Edinburgh, UK +1
From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems
Apr 21, 2026Quang-Huy Nguyen, Thanh-Hai Nguyen, Khac-Manh Thai +6Counterfactual Evaluation Counterfactual Explanations
VNU University of Engineering and Technology · Delft University of Technology
TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations
Apr 14, 2026Ashmi Banerjee, Adithi Satish, Wolfgang Wörndl +1Large Language Model-Based Recommendation Explainable Recommendation
Technical University of Munich, Munich, Germany · Polytechnic University of Bari, Bari, Italy

Oct 27, 2026J/K move · Enter open · S save

The Vocabulary Is Already Here: Adapting Pretrained Interpretable Features for Generative Recommendation

Ziluowen Luo, Ruochen Liu, Jun Yin, Hao Yan+5

Central South University Changsha, Hunan, China · The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China · Hong Kong Polytechnic University Hongkong, China+4

Generative Recommendation Semantic ID Recommendation Explainable Recommendation

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Generative recommendation (GR) predicts the next item through its semantic ID (SID). Existing methods commonly fit SID vocabularies to pretrained language model (PLM) representations of item descriptions, incurring additional training costs and making vocabulary coverage dependent on the fitting domains. We observe that pretrained sparse autoencoders expose PLM features with human-interpretable semantic annotations, allowing feature indices to serve as a reusable SID vocabulary. However, reusing this vocabulary requires preserving item distinctions under compact token budgets and learning to favor target items over candidates with shared dominant features. It also requires extending feature interpretability from item content to retrieval decisions. To address these challenges, we propose API, a framework that Adapts the Pretrained Interpretable vocabulary for effective and explainable GR without additional vocabulary learning. Within API, Pretrained Interpretable IDs (PIDs) retain selected feature indices and relative activation strengths for item discrimination. With PIDs as supervision, the Semantic Prediction Adapter (SPA) learns user preferences from interaction histories through joint feature-index and strength prediction. SPA combines both predictions to score candidate items, with item-level supervision encouraging the observed next item to outrank its alternatives. API further explains candidate score differences through feature contributions paired with their pretrained semantic annotations. Extensive experiments show that API achieves competitive performance across backbones and decoding schemes, while preserving item distinctions under compact token budgets and enabling explainable retrieval.