Recommender Systems

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32 papers in the last four weeks, up 28% on the four weeks before. 0.2% of all new papers.

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Balancing Trial and Reorder: A Hybrid Sequential Transformer-GBDT Ranker for On-Demand Delivery
Sep 14, 2026Marcel Kurovski, Attila Nagy, Steffen Klempau +1Sequential Recommendation Learning to Rank
Wolt (DoorDash, Inc.) · Munich, Germany · Berlin, Germany +1
Self-Evolving Memory for Generative Recommendation
Sep 14, 2026Xinyu Lin, Zhuosong Jiang, Zixiao Suo +12Memory-Augmented Neural Networks Recommender Systems
National University of Singapore · Meta AI
Recommendation Retrievers Need Verifiers: Universal Generative Reranking for Sequential Recommendations
Sep 10, 2026Benyu Zhang, Qiang Zhang, Rui Li +3Sequential Recommendation Recommender Systems
Meta MRS
On the Regularization Landscape for the Linear Recommendation Models
Sep 10, 2026Dong Li, Zhenming Liu, Ruoming Jin +4Nuclear Norm Minimization Low-Rank Approximation
Kent State University, USA · College of William and Mary, USA · iLambda, USA
Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System
Sep 10, 2026Ming Li, Dai Li, Xuying Ning +11Recommender Systems Long-Horizon LLM Agents
Meta · University of Illinois Urbana-Champaign
An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems
Sep 9, 2026Quoc Viet Nguyen, Trinh Pham, Viet Huynh +4Adversarial Attacks Recommender Systems
Griffith University, Australia · Edith Cowan University, Australia · The University of Queensland, Australia +1
ReCite: Agentic Reasoning for Faithful Citation
Sep 8, 2026Yuyang Huang, Bobo Li, Jiajia Song +4Citation Verification Agentic RAG
School of Cyber Science and Engineering, Wuhan University · National University of Singapore
Quantifying the Engagement Trap: Impact of Short-form Video Recommender Systems on Users with ADHD
Sep 7, 2026Vedad Misirlic, Gregor Mayr, Elisabeth LexMental Health Human-Centered AI
Graz University of Technology, Austria
Continual Graph Memory for Adaptive Recommendation under Intent Drift
Sep 4, 2026Hao Nguyen Ngoc, Tung Nguyen, Nguyen Thi Hanh +2Concept Drift Graph-Based Agent Memory
Phenikaa University, Vietnam · Hanoi University of Science and Technology, Vietnam · University of Technology Sydney, Australia
CORAL: An LLM-Native Harness for Production Recommender Systems
Sep 2, 2026Muhammad Rafay Azhar, Yuhang Zhou, Gilbert Jiang +7Recommender Systems LLM Agents
Meta AI
Training seeds and model-selection stability in recommender-system evaluation
Sep 2, 2026Juan Manuel Rodriguez, Oleg Lesota, Antonela TommaselRecommender Systems ML Reproducibility
Aalborg University Aalborg, Denmark · Johannes Kepler University Linz Linz, Austria · ISISTAN, CONICET-UNCPBA Tandil, Argentina
DMRL: Document-Mediated Reinforcement Learning for Skill Optimization in Advertising Recommendation
Sep 2, 2026Wei Zhang, Hongji Li, Song Sun +4Recommender Systems RL for Language Models
Shanghai Jiao Tong University · Kuaishou Technology
GenCAR: Generative Counterfactual Alignment with Risk-Controlled Selection for Out-of-Distribution Recommendation
Sep 2, 2026Qianqian Wang, Yunshan Li, Jiawen Zeng +2Recommender Systems FDR Control
aShenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, Southern University of Science and Technology, Shenzhen, China · bDepartment of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China · cSchool of Engineering and Applied Science, University of Pennsylvania, Philadelphia, USA
Beyond Modality Harmony: Orthogonal Purification and Topology-Guided MoE for Conflict-Aware Multimodal Recommendation
Sep 2, 2026Jialin Liu, Zhaorui Zhang, Ray C. C. CheungRecommender Systems Multimodal Recommendation
City University of Hong Kong Hong Kong, Hong Kong · The Hong Kong Polytechnic University Hong Kong, Hong Kong
From Language to Behavior: Scaling Sequence Transformers for Industrial Recommendation Ranking with Rec-Native Designs
Sep 1, 2026Jie Chen, Xiangqian Yu, Yanchao Lian +9Sequential Recommendation Learning to Rank
ByteDance
User Representation via Cross Multi-source Behavior Pre-training for Mobile Games
Sep 1, 2026Chengqi Yang, Yiran Qiao, Feng Liu +7Self-Supervised Pre-Training User Modeling
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, China. · University of Chinese Academy of Sciences, CAS, Beijing, China. · OPPO Research Institute, Shenzhen, China +1
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 Large Language Model-Based Recommendation
College of Computer Science, Sichuan University · Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China · Institute of Data Science, National University of Singapore +1
Retrieval, Scoring, and Decoding Shape Performance and Stability in LLM-based Conversational Recommendation
Aug 31, 2026Ante Kapetanovic, Tomislav Duricic, Andro Mercep +1LLM Evaluation LLM Reranking
Infobip · Split, Croatia · Zagreb, Croatia
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
Timing-Aware Repurchase Prediction for Web-Scale E-Commerce: Survival Models for Multi-Surface Grocery Recommendation
Aug 28, 2026Akshay Kekuda, Shreeranjani Srirangamsridharan, Ishan Bhatt +5Probability Calibration Survival Analysis
Walmart Global Tech · Bellevue, WA, USA · Sunnyvale, CA, USA
Recovering Expert Critic-Sourced Network Adjacency between Musical Artists from Acoustic Distributions: A Construct-Validity Approach
Aug 27, 2026Elena Badillo-Goicoechea, Fengfeng HeCold-Start Recommendation Link Prediction
University of Chicago Chicago, Illinois, USA
PRQ-KMeans: Projection Residual Quantization for Semantic ID Tokenization
Aug 25, 2026Yunxiao Luo, Siyuan Wang, Ben Chen +2Residual VQ Generative Retrieval
1Kuaishou Technology
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
Dear Algo: A Precision-First Agentic Intent Layer for Unified Search and Recommendation
Aug 16, 2026Rui Wang, Jiazhou Wang, Zheng Wei +16Language Model Steering Recommender Systems
Meta Platforms Inc. USA · Google DeepMind, USA
Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation
Aug 12, 2026Kangning Zhang, Haotian Fang, Xukun Luo +6Sequential Recommendation Recommender Systems
Shanghai Jiao Tong University Shanghai, China · Mei Tuan Beijing, China
From Prompting to Behavioral Alignment: Personalized LLM Judges for Recommendation Evaluation
Aug 11, 2026Alireza S. Ziabari, Kat Ellis, Colleen Chan +1LLM Alignment Recommender Systems
Netflix Los Angeles, USA · Netflix Los Gatos, USA
Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces
Aug 11, 2026Mengyu Chen, Feiyu Lu, Chun-Fu Chen +2Theory of Mind User Modeling
JPMorganChase New York City, NY, USA · JPMorganChase London, UK · JPMorganChase Palo Alto, CA, USA
Batch Size or Negatives? A Selection Rule for Memory-Constrained Recommender Training
Aug 11, 2026Artyom Sabitov, Daniil Volkov, Alexey ZaytsevMemory-Efficient Optimization Recommender Systems
Applied AI Institute, Moscow, Russia Risk department
TimeRoute: Time-Aware Modality Routing and Diffusion for Multi-Modal Recommendation
Aug 11, 2026Pengyu Zhang, Yangqin Jiang, Klim Zaporojets +2Recommender Systems Multimodal Recommendation
University of Amsterdam Amsterdam, The Netherlands · University of Hong Kong Hong Kong, China · Aarhus University Aarhus, Denmark
FedCGR: Federated Cross-Domain Generative Recommendation
Aug 11, 2026Zhuodong Liu, Hugen Lv, Xiangyu Li +2Recommender Systems Generative Recommendation
Beijing Jiaotong University China · Shanghai Jiao Tong University China · University of Malaya Malaysia +1

Sep 14, 2026, cs.IRJ/K move · Enter open · S save

Balancing Trial and Reorder: A Hybrid Sequential Transformer-GBDT Ranker for On-Demand Delivery

Marcel Kurovski, Attila Nagy, Steffen Klempau, Aleksandr Fedintsev

Wolt (DoorDash, Inc.) · Munich, Germany · Berlin, Germany+1

Sequential Recommendation Learning to Rank Recommender Systems

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On a delivery platform, personalized store ranking greatly influences what users find and order. Unlike digital-only domains, candidate stores are local and bound by real-time availability and delivery operations. One central modeling tension is between surfacing new stores for trial and preserving ranking quality for sessions with reorder intent. We present Universal Venue Ranker (UVR), a production system deployed at Wolt that pairs a bidirectional transformer encoder for sequential user modeling with a GBDT ranker integrating contextual, user, and store features. Trained across all stores and domains of a country while enforcing local delivery constraints at inference, UVR replaces four previously separate ranking models (three for restaurants, one for retail) with a single unified system. Label smoothing and trial-biased sample weighting steer the model toward new stores, lifting offline trial MRR by +12% to +30% over production while regressing reorder MRR in five of six countries. These regressions leave Global CVR, our core online metric, which blends trial and reorder sessions, statistically unchanged. We validate UVR in three consecutive A/B tests, the first two across Wolt's largest operating markets and the third spanning all operating countries and both domains. UVR V1 delivers +5.5% Merchant Trial Rate and +0.16% Global CVR over the previous production ranker; V2 adds a further +0.45% Merchant Trial Rate on top; and V3, our cross-domain unification of the restaurant and retail rankers, adds a further +1.31% Retail Merchant Trial Rate, together accounting for substantial incremental gross order value and a materially simplified serving stack.

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