Generative Recommendation

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12 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

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Latest papers 62

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On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies
Jun 15, 2026Sunwoo Kim, Sunkyung Lee, Clark Mingxuan Ju +5Sequential Recommendation Memorization in Language Models
1KAIST · 2Sungkyunkwan University · 3Snap Inc.
HoloRec: Holistic Encoding and Interleaved Reasoning for Generative Recommendation
Jun 13, 2026Shuqi Zhao, Jingsong Su, Xiang Liu +9Hierarchical Representation Learning Generative Recommendation
Institute of Information Engineering, Chinese Academy of Sciences Beijing, China · School of Artificial Intelligence, Beijing Normal University Beijing, China · JD.com,Beijing,China
ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation
Jun 12, 2026Dongdong Nian, Dongqi Fu, Chenliang Xu +4Semantic ID Recommendation Sequential Recommendation
University of Rochester · Meta MRS · MBZUAI
Implicit Reasoning for Large Language Model-based Generative Recommendation
Jun 12, 2026Yinhan He, Liam Collins, Bhuvesh Kumar +3Implicit Reasoning in Language Models Large Language Model-Based Recommendation
University of Virginia, Charlottesville, VA, USA · Snap Inc., Santa Monica, CA, USA
One Polluted Page Is Enough: Evaluating Web Content Pollution in Generative Recommenders
Jun 11, 2026Minghao Luo, Liang ChenLLM Evaluation RAG Security
The Chinese University of Hong Kong
DiffCold: A Diffusion-based Generative Model for Cold-Start Item Recommendation
Jun 10, 2026Kangning Zhang, Yingjie Qin, Weinan Zhang +2Cold-Start Recommendation Recommender Systems
Shanghai Jiao Tong University, Shanghai, China · Xiaohongshu Inc., Shanghai, China
MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention
Jun 8, 2026Asiful Arefeen, Carol Johnston, Hassan GhasemzadehClinical Decision Support Generative Recommendation
College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA
Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation
Jun 7, 2026Kewei Xu, Junbo Qi, Yanyan Zou +3Group Relative Policy Optimization Recommender Systems
Waseda University Tokyo, Japan · University of Electronic Science and Technology of China Chengdu, China
TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
Jun 5, 2026Ziheng Chen, Jiali Cheng, Zezhong Fan +4Sequential Recommendation Concept Erasure
Stony Brook University · University of Massachusetts Lowell · Columbia University +3
OneReason Technical Report
Jun 4, 2026OneRec Team, Biao Yang, Boyang Ding +81Sequential Recommendation CoT Reasoning
Time-Aware Diffusion based on Preference Disentanglement for Generative Recommendation
Jun 1, 2026Bangguo Zhu, Peng Huo, Yuanbo Zhao +3Sequential Recommendation Recommender Systems
Central South University Changsha, China · National Super Computing Center Tianjin, China · Renmin University of China Beijing, China +1
LLMs Need Encoders for Semantic IDs Too
May 29, 2026Xiangyi Chen, Zelun Wang, Xinyi Li +3Memory-Augmented Language Models Generative Recommendation
Pinterest · United States
Generative Spatiotemporal Intent Sequence Recommendation via Implicit Reasoning in Amap
May 27, 2026Sicong Wang, Ruiting Dong, Yue Liu +7Spatiotemporal Reasoning CoT Distillation
AMAP, Alibaba Group Beijing, China
DeGRe: Dense-supervised Generative Reranking for Recommendation
May 25, 2026Chaotian Song, Jingyao Zhang, Chenghao Chen +6Learning to Rank Generative Recommendation
College of Software, Zhejiang University Hangzhou, China · Rajax Network Technology, Taobao Shangou of Alibaba Hangzhou, China · Rajax Network Technology, Taobao Shangou of Alibaba Beijing, China +1
Divergent Recommendations, Convergent Diagnoses: Cross-Provider Failure-Mode Convergence in AI Commercial Recommendation
May 22, 2026Will Jack, Noah Lehman, Keller Maloney +1Generative Engine Optimization Large Language Model-Based Recommendation
Unusual
SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation
May 18, 2026Wei Chen, Xingyu Guo, Shuang Li +6Recommender Systems Multimodal Recommendation
School of Artificial Intelligence, Beihang University, Beijing, China · School of Computer Science and Engineering, Beihang University, Beijing, China
Learning Variable-Length Tokenization for Generative Recommendation
May 18, 2026Minhao Wang, Bowen Wu, Wei ZhangResidual VQ Generative Recommendation
East China Normal University · Shanghai, China · & Shanghai Innovation Institute
SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation
May 17, 2026Zaiyi Zheng, Guanghui Min, Yaochen Zhu +4RL for Language Model Reasoning Credit Assignment in RL
University of Virginia · 2Nokia
Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders
May 16, 2026Jun Yin, Bangguo Zhu, Peng Huo +5Recommender Systems Generative Recommendation
School of Computer Science and Engineering, Central South University, Changsha, China · National Super Computing Center, Tianjin, China · Faculty of Data Science, City University of Macau, Macau, China +1
Asymmetric Generative Recommendation via Multi-Expert Projection and Multi-Faceted Hierarchical Quantization
May 14, 2026Bin Huang, Xin Wang, Junwei Pan +6Vector Quantization Recommender Systems
DCST, Tsinghua University Beijing, China · DCST, BNRist, Tsinghua University Beijing, China · Tencent Shenzhen, China
TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation
May 13, 2026Huichao Chai, Zhixin Wu, Xuemiao Li +8Communication-Efficient Distributed Training Generative Recommendation
Huawei
Why Users Go There: World Knowledge-Augmented Generative Next POI Recommendation
May 12, 2026Qiuyu Ding, Heng-Da Xu, Wei Zhang +4Sequential Recommendation Knowledge Augmentation for Language Models
Amap, Alibaba Group · Xi’an Jiaotong University
Conditional Memory Enhanced Item Representation for Generative Recommendation
May 12, 2026Ziwei Liu, Yejing Wang, Shengyu Zhou +2Semantic ID Recommendation Representation Learning
City University of Hong Kong Hong Kong, China · Independent Researcher Beijing, China · Tsinghua University Beijing, China
A Cascaded Generative Approach for e-Commerce Recommendations
May 11, 2026Moein Hasani, Hamidreza Shahidi, Trace Levinson +4Recommender Systems Large Language Model-Based Recommendation
Ambience Healthcare San Francisco, USA
One Pool, Two Caches: Adaptive HBM Partitioning for Accelerating Generative Recommender Serving
May 6, 2026Wenjun Yu, Shuguang Han, Amelie Chi ZhouLLM Inference Efficiency GPU Acceleration
Hong Kong Baptist University · Hong Kong, China · Alibaba Inc +1
Position-Aware Drafting for Inference Acceleration in LLM-Based Generative List-Wise Recommendation
Apr 30, 2026Jiaju Chen, Chongming Gao, Chenxiao Fan +4Speculative Decoding LLM Inference Acceleration
University of Science and Technology of China, Hefei, China · Independent Researcher
Action-Aware Generative Sequence Modeling for Short Video Recommendation
Apr 28, 2026Wenhao Li, Zihan Lin, Zhengxiao Guo +7Sequential Recommendation User Preference Modeling
Kuaishou Inc. · Beijing, China · Unaffiliated +2
Modeling Behavioral Intensity and Transitions for Generative Recommendation
Apr 27, 2026Wenxuan Yang, Xiaoyang Xu, Hanyu Zhang +3Sequential Recommendation Recommender Systems
Ant Group · Fudan University
MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches
Apr 24, 2026Xin Wang, Chi Ma, Shaobin Chen +14KV-Cache Offloading KV Caching
School of Computer Science, Wuhan University · Meituan · Nvidia
GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation
Apr 16, 2026Yanyan Zou, Junbo Qi, Lunsong Huang +7Generative Retrieval Recommender Systems
JD.com Beijing, China · Waseda University Tokyo, Japan

Jun 15, 2026, cs.IRJ/K move · Enter open · S save

On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies

Sunwoo Kim, Sunkyung Lee, Clark Mingxuan Ju, Donald Loveland+4

1KAIST · 2Sungkyunkwan University · 3Snap Inc.

Sequential Recommendation Memorization in Language Models Large Language Model-Based Recommendation Generative Recommendation

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Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been increasingly adopted for GR, as their rich pretrained knowledge is expected to help them generalize beyond common user behavior patterns that traditional memorization-oriented baselines can capture. However, existing LLM-based GR works largely ignore LLMs' well-known tendency to memorize, which, if present in LLMs fine-tuned for GR, would restrict their utilization of pretrained knowledge. In this work, we investigate this concern by examining one-hop memorization, where a model recommends items that are direct successors of items in the training data. We show that LLMs do this more than non-LLM-based GR models-in fact, the vast majority of their gains over GR baselines are actually on users whose target items can be predicted through one-hop memorization. We intuit that improving performance on the remaining users requires LLMs to learn richer item-item relations beyond one-hop transitions. To achieve this, we propose IIRG, a novel training strategy that teaches LLMs to capture: (1) collaborative relations derived from item co-occurrences across multiple hops in user sequences, and (2) semantic relations among items with similar themes, both of which can serve as useful recommendation signals. We show that IIRG significantly improves over LLMs trained solely with standard next-item prediction, with especially large gains for users whose test items are not covered by train-time one-hop transitions.

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Latest papers 62