Recommender Systems

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4 papers in the last 28 days · 0.1% of indexed attention

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Period ending 2026-09-14

5 new papers

A weekly snapshot of new work published in Recommender Systems.

Period ending 2026-09-07

1 new paper

A weekly snapshot of new work published in Recommender Systems.

53 papers

Latest in Recommender Systems

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Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles
Sep 17, 2026Noah Mamié, Laurin van den BerghRecommender Systems Profile
LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era
Sep 16, 2026Venkat Srinivas, Chenzhang He, Sam Woodmansee +59Recommender Systems Recommendation
AURA: Agentic Diagnosis and Refinement for Production Recommender Systems at Scale
Sep 15, 2026SungGeun Kim, Abhinav Narain, Daniel NemirovskyRecommender Systems Recommendation
An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems
Sep 9, 2026Quoc Viet Nguyen, Trinh Pham, Viet Huynh +4Recommender Systems Agentic Tool Use
Quantifying the Engagement Trap: Impact of Short-form Video Recommender Systems on Users with ADHD
Sep 7, 2026Vedad Misirlic, Gregor Mayr, Elisabeth LexRecommender Systems Large-Scale Recommendation Systems
Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search
Sep 1, 2026Jincheng Zhang, Chen Huang, Wenqiang Lei +2Recommender Systems Model Preference
The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations
Aug 18, 2026Emma Yanyang Kong, JJ Tan, Ishan Gupta +8Llm-As-A-Judge Large Language Model Decision-Making
A Recommendation System Approach for Interference-Robust Sensor Subset Selection
Aug 11, 2026Kaan Buyukkalayci, Kyle Pak, Merve Karakas +1Multi-Sensor Fusion Multi-Object Tracking
Batch Size or Negatives? A Selection Rule for Memory-Constrained Recommender Training
Aug 11, 2026Artyom Sabitov, Daniil Volkov, Alexey ZaytsevRecommender Systems Batch Size
Stay or Stray - A Dynamical Systems Viewpoint of Popularity Bias
Aug 11, 2026Sarvesh Shashidhar, Lankireddy Prabhat, Arpit Agarwal +3Popularity Recommender Systems
Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging
Aug 11, 2026Linh Dieu Le, Tong Chen, Shazia Sadiq +3Continual Model Merging Recommender Systems
Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation
Aug 6, 2026Ziyun Xu, Bosen Ding, Yue Zhang +10Recommender Systems Recommendation
Compass: Continuously Aligning Social Media Feeds via In-Situ Reflections
Aug 4, 2026Aadit Barua, Leijie Wang, Amy X. ZhangRecommender Systems Social Media
ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation
Jul 30, 2026Yuxin Chen, Liang Luo, Buyun Zhang +44Large-Scale Recommendation Systems Recommender Systems
PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest
Jul 24, 2026Olafur Gudmundsson, Bo Zhao, Huayi Liao +15Recommender Systems Funnel Efficiency
Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation
Jul 23, 2026Jie Peng, Yanping Zheng, Zhewei Zhe +3Recommendation Recommender Systems
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
Jul 20, 2026Yongsen Zheng, Ruilin Xu, Ziliang Chen +4Recommender Systems Recommendation
Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning
Jul 15, 2026Dingsu Wang, Filip Ryzner, Kelly He +12Recommender Systems Analytics
Can We Steer the Black-Box? Towards Controllability-Centric Evaluation of Recommender Systems with Collaborative Agents
Jul 15, 2026Jiwen Zhou, Xiang Liu, Mingming Li +5Recommender Systems Sequential Recommendation
MESH: Scaling Up Retrieval with Heterogeneous Content Unification
Jul 14, 2026Jiaxing Qu, Yilin Chen, Junpeng Hou +4Recommender Systems Neural Scaling Laws
Normative Alignment of Recommender Systems via Internal Label Shift
Jul 12, 2026Johannes Kruse, Kasper Lindskow, Michael Riis Andersen +4Recommender Systems Preference Alignment
ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation
Jul 12, 2026Johannes Kruse, Ryotaro Shimizu, Kasper Lindskow +4Recommender Systems Headlines
PIT-SUN: A Deployable Empirical Marginal Transform Framework with Expectation-Consistent Recovery for Regression in Recommender Systems
Jul 9, 2026Mingyu Zhao, Zhaohan Li, Zhenxiong Miao +4Posterior Estimators
Beyond Accuracy: How Humans Evaluate Legally Correct but Socially Controversial Legal Advice from Machines
Jul 6, 2026Benjamin Minhao Chen, Zhiyu LiLegal Domain Human Judgment
Toward Personalized Social Robots for Child Well-being: Data Requirement Principles from a Recommender-System Perspective
Jul 6, 2026Jin Huang, Eric Nichols, Fethiye Irmak Dogan +1Children Profile
Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
Jul 5, 2026Xinyu Lin, Yashar Deldjoo, Sunhao Dai +7Recommender Systems Agentic Systems
Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search
Jul 1, 2026Longfeng Wu, Yao Zhou, Tong Zeng +5Recommender Systems Neural Architecture Search
PAPA: Online Personalized Active Preference Alignment
Jul 1, 2026Anindya Sarkar, Nasik Muhammad Nafi, Isaac Lyngaas +2Preference Alignment Training-Free Reward Alignment
Fairness Attacks on Recommender Systems
Jun 27, 2026Yanan Wang, Yong GeRecommender Systems Fairness-Aware Learning
Denoising Implicit Feedback for Cold-start Recommendation
Jun 17, 2026Gaode Chen, Shicheng Wang, Shikun Li +8Recommender Systems Cold-Start
BAHSD: Bridging the Long-tail Gap via Adaptive Distillation in Black-box Sequential Recommendation
Jun 2, 2026Xi Zhou, Famin Wu, Mingming Li +4Multimodal Recommendation Recommender Systems
LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems
May 31, 2026Yihong Huang, Chen Chu, Fei Chen +3Feature Selection Recommender Systems
Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
May 27, 2026Weizhi Zhang, Wooseong Yang, Yuxin Cui +9Recommender Systems Large-Scale Recommendation Systems
Context Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems
May 24, 2026Yevgeny TkachLarge-Scale Recommendation Systems Recommender Systems
Robust Recommendation from Noisy Implicit Feedback: A GMM-Weighted Bayes-label Transition Matrix Framework
May 20, 2026Zongyu Li, Xuanyu Liu, Gongce Cao +3Noisy Labels Recommender Systems
Spectral bandits for smooth graph functions with applications in recommender systems
May 19, 2026Tomáš Kocák, Michal Valko, Rémi Munos +2Large-Scale Recommendation Systems Recommender Systems
When to Ask a Question: Understanding Communication Strategies in Generative AI Tools
May 11, 2026Charlotte Park, Kate Donahue, Manish RaghavanGenerative Artificial Intelligence Elicitation
TRACE: Tourism Recommendation with Accountable Citation Evidence
May 8, 2026Zixu Zhao, Sijin Wang, Yu Hou +6Recommender Systems Tour
Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework
May 8, 2026Nipun B Nair, Tongtong Wu, Weiqing WangUser Simulation Recommender Systems
A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
May 8, 2026Yichu Zhou, Mehdi Ben Ayed, Lin Yang +9Recommender Systems Pareto Frontier
Bridging Textual Profiles and Latent User Embeddings for Personalization
May 7, 2026Zhaoxuan Tan, Xiang Zhai, Yan Zhu +2Recommender Systems Multimodal Recommendation
One Pool, Two Caches: Adaptive HBM Partitioning for Accelerating Generative Recommender Serving
May 6, 2026Wenjun Yu, Shuguang Han, Amelie Chi ZhouGraphics Processing Unit Memory Sequential Recommendation
Decision-aware User Simulation Agent for Evaluating Conversational Recommender Systems
May 5, 2026Yuan-Chi Li, Li-Chi Chen, Sung-Yi Wu +2User Simulation Multi-Agent Simulations
The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems
Apr 29, 2026Pedro R. Pires, Gregorio F. Azevedo, Rafael T. Sereicikas +2Recommender Systems Large-Scale Recommendation Systems
Modeling Behavioral Intensity and Transitions for Generative Recommendation
Apr 27, 2026Wenxuan Yang, Xiaoyang Xu, Hanyu Zhang +3Multimodal Recommendation Recommender Systems
Sharpness-Aware Poisoning: Enhancing Transferability of Injective Attacks on Recommender Systems
Apr 24, 2026Junsong Xie, Yonghui Yang, Pengyang Shao +1Recommender Systems Large-Scale Recommendation Systems
Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation
Apr 23, 2026Nikita Severin, Danil Kartushov, Vladislav Urzhumov +8Recommender Systems Sequence Modeling
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 +6Recommender Systems Counterfactual Explanation
GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation
Apr 16, 2026Yanyan Zou, Junbo Qi, Lunsong Huang +7Large-Scale Recommendation Systems Recommender Systems
CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations
Apr 16, 2026Xiping Li, Aier Yang, Jianghong Ma +4Recommender Systems Recommendation
Towards Reliable Recommender Systems for Rating Data
Dec 30, 2024Aurore Archimbaud, Andreas Alfons, Ines WilmsRecommender Systems Large-Scale Recommendation Systems
Ensemble Learning Based Classification Algorithm Recommendation
Jan 15, 2021Guangtao Wang, Qinbao Song, Xiaoyan Zhu +1Ensemble Sequential Recommendation
Membership Inference Attacks on Recommender System: A Survey
Date pendingJiajie He, Xintong Chen, Xinyang Fang +4Membership Inference Attacks Recommender Systems

Sep 17, 2026 · cs.IRJ/K move · Enter open · S save

Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles

Noah Mamié, Laurin van den Bergh

In this reproducibility study, we investigate the transparency and scrutability of recommender systems enhanced by incorporating generated natural-language user profiles that represent user preferences. The original paper explores the synthesis of user profiles from raw user-generated review text across domains such as movies and accommodations (Amazon Movies & TV, TripAdvisor). Crucially, these natural-language user profiles enable direct user interaction and intervention, allowing users to customize recommendations by correcting misattributed preferences or addressing cold-start settings. We successfully reproduce the core findings of the original study. Additionally, we extend the evaluation by conducting systematic context ablation experiments, multi-seed stability across five distinct random seeds to establish statistical reliability, and a mechanistic interpretability analysis using the nnsight framework to probe internal model representations under counterfactual profile perturbations. Our findings verify the original paper's claim that User Profile Recommendation (UPR) achieves competitive performance under its test-set reranking protocol and makes recommendations more transparent. Perturbing the natural-language profiles does change predictions, but it shifts predicted ratings uniformly across genres with no detectable genre-selective effect, leaving rankings unchanged even under direct activation steering. We trace this back to the rating-regression objective rather than the profile interface, with ranking-objective models clearly exceeding in this task.

Recommender Systems Profile Transparency Mechanistic Interpretability Counterfactual Reasoning

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