Large Language Model-Based Recommendation

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11 papers in the last four weeks, down 15% on the four weeks before. 0.1% of all new papers.

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Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking
May 22, 2026Wonkyun Kim, Sehyun Bae, Kwanki Ahn +5Recommender Systems Large Language Model-Based Recommendation
Samsung Electronics Republic of Korea · Samsung Electronics India
LLM Retrieval for Stable and Predictable Ad Recommendations
May 21, 2026Vinodh Kumar Sunkara, Satheeshkumar Karuppusamy, Hangjun Xu +13Online Advertising Recommender Systems
Meta Platforms, Inc. Menlo Park and New York, USA
Don't Let Bandit Feedback Pull Continual LLM-Recommender Updates Off Target
May 17, 2026Taesan Kim, Hyeongjun Yun, Jaegul Choo +1Continual Learning for LLMs Recommender Systems
1SK Telecom · 2KAIST
RxEval: A Prescription-Level Benchmark for Evaluating LLM Medication Recommendation
May 14, 2026Shuhao Chen, Weisen Jiang, Changmiao Wang +4Healthcare Clinical Decision Support
The Hong Kong University of Science and Technology · Southern University of Science and Technology · The Chinese University of Hong Kong +2
Agentic Recommender System with Hierarchical Belief-State Memory
May 14, 2026Xiang Shen, Yuhang Zhou, Yifan Wu +8LLM Agent Memory Recommender Systems
Meta Recommendation Systems (MRS)
Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMs
May 12, 2026Andreas Maier, Jeta Sopa, Gozde Gul Sahin +2LLM Auditing LLM Prompting
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
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
RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents
May 11, 2026Imad Aouali, Flavian Vasile, Otmane Sakhi +2Tool-Use Evaluation LLM Agent Evaluation
Criteo AI Lab
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
An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations
May 11, 2026Yibin Wang, Yanjie Yang, Grace Melo Guerrero +2Retrieval-Augmented Generation Recommender Systems
Department of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Dallas, TX, USA · Department of Agricultural Economics, Texas A&M University, College Station, TX, USA
Intent-Driven Semantic ID Generation for Grounded Conversational News Recommendation
May 8, 2026Hongyang Su, Beibei Kong, Lei Cheng +3Semantic ID Recommendation LLM Grounding
Platform and Content Group, Tencent, Shenzhen, China · Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
SkillSelect-Serve: QoS-Aware Budgeted Skill Service Recommendation for LLM Agents
May 8, 2026Jingyuan Zheng, Dongjing Wang, Xin Zhang +7LLM Agent Skill Retrieval LLM Agents
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China · Sanya Traditional Chinese Medicine Hospital, Sanya, 572000, China · HDU-ITMO Joint Institute, Hangzhou Dianzi University +3
Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework
May 8, 2026Nipun B Nair, Tongtong Wu, Weiqing WangAutomatic Prompt Optimization Recommender Systems
Monash University, Australia
DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation
May 8, 2026Xinchi Zou, Tongzhenzhi Su, Jianjun Li +4Recommender Systems Large Language Model-Based Recommendation
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China · School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China · Laboratory for Artificial Intelligence and New Forms of Education, Central China Normal University, Wuhan, China
RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
May 8, 2026Shijun Li, Pranav Belligundu, Tianxin Wei +3Agentic RAG Recommender Systems
The University of Texas at Austin · University of Illinois at Chicago · Capital One AI Foundations +1
Bridging Textual Profiles and Latent User Embeddings for Personalization
May 7, 2026Zhaoxuan Tan, Xiang Zhai, Yan Zhu +2Sequential Recommendation Recommender Systems
University of Notre Dame · 2Google · * This work was done while Zhaoxuan was a student researcher at Google.
DynamicPO: Dynamic Preference Optimization for Recommendation
May 1, 2026Xingyu Hu, Kai Zhang, Jiancan Wu +7Hard Negative Mining Preference Optimization
University of Science and Technology of China, Hefei, China · Shanghai Innovation Institute, Shanghai, China · Meituan, Chengdu, China
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
One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation
Apr 30, 2026Ethan Bito, Yongli Ren, Estrid HeLearning to Rank LLM Reranking
RMIT University, Melbourne, Australia
A Reproducibility Analysis of PO4ISR: Diagnosing and Mitigating Semantic Drift in LLM-Based Session Recommendation
Apr 29, 2026Aditya Tiwari, Konduri Naga Lakshmi Rekha, Rajesh Kumar MundotiyaSequential Recommendation LLM Prompting
MATRA Lab Department of Computer Science and Engineering Indian Institute of Technology Bhilai Chhattisgarh, India 491002
An Investigation of Linguistic Biases in LLM-Based Recommendations
Apr 28, 2026Nitin Venkateswaran, Jason Ang, Deep Adhikari +1Language Model Bias Evaluation Linguistic Bias in Language Models
University of Florida
Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation
Apr 23, 2026Nikita Severin, Danil Kartushov, Vladislav Urzhumov +8Sequential Recommendation Language Model Distillation
Independent researcher, Belgrade, Serbia · ITMO University, Saint Petersburg, Russia · Innopolis University, Innopolis, Russia +3
Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation
Apr 23, 2026Minping Chen, Bing Xu, Zulong Chen +4Data Augmentation Mixture of Experts
HKUST (GZ) · Alibaba Group · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) +3
On Reasoning Behind Next Occupation Recommendation
Apr 23, 2026Shan Dong, Palakorn Achananuparp, Hieu Hien Mai +3Large Language Model-Based Recommendation Language Model Post-Training
Singapore Management University · Columbia University
Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation
Apr 22, 2026Dongding Lin, Jian Wang, Yongqi Li +1Recommender Systems Large Language Model-Based Recommendation
Department of Computing, The Hong Kong Polytechnic University · College of Computer Science, Sichuan University
Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations
Apr 20, 2026Yunjia Xi, Menghui Zhu, Jianghao Lin +4LLM Compression Embedding Compression
Shanghai Jiao Tong University Shanghai, China · Huawei Noah’s Ark Lab Shanghai, China · Antai College of Economics and Management, Shanghai Jiao Tong University Shanghai, China
GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation
Apr 20, 2026Lin Mu, Guoji Wang, Li Ni +4Low-Rank Adaptation Large Language Model-Based Recommendation
Anhui University · Hefei University · University of Science and Technology of China
Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation
Apr 17, 2026Nicolò Pagan, Christopher Barrie, Chris Andrew Bail +1LLM Prompting Social Bias in Language Models
University of Zurich, Department of Informatics, Zurich, CH · New York University, Department of Sociology, New York, USA · University of Oxford, Department of Sociology, Oxford, UK +2
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
IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu
Mar 2, 2026Yubin Zhang, Haiming Xu, Guillaume Salha-Galvan +6Cold-Start Recommendation Multimodal Embedding
Xiaohongshu Inc. Shanghai, China · Shanghai Jiao Tong University Shanghai, China · Fudan University Shanghai, China

May 22, 2026, cs.IRJ/K move · Enter open · S save

Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking

Wonkyun Kim, Sehyun Bae, Kwanki Ahn, Mungyu Bae+4

Samsung Electronics Republic of Korea · Samsung Electronics India

Recommender Systems Large Language Model-Based Recommendation User Preference Modeling

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Industrial video-on-demand (VOD) recommenders need richer content understanding, but LLM-as-reranker designs repeat prompt construction, token generation, model invocation, output parsing, and fallback handling for each request. In high-volume latency-sensitive services, these request-time operations complicate throughput planning, tail-latency control, capacity isolation, and predictable operation. This paper presents Ocean4Rec, a reranking layer that uses an LLM only offline to materialize item OCEAN profiles from content metadata. Items are mapped into Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism scores, while user profiles are built by time-decayed aggregation of recently clicked and deep-linked items in the same five-dimensional space. At request time, Ocean4Rec joins precomputed item profiles, user profiles, base recommender scores, and catalog recency, then performs numeric reranking without an LLM call. On anonymized Samsung Smart TV VOD logs, same-candidate Top1000 temporal-holdout offline evaluations show that Ocean4Rec improves NDCG@20 over a stronger non-OCEAN Base+Recency ordering by 7.6% for an NCF generator and 61.5% for a LightGCN generator. HR@20 is inconclusive for NCF and improves by 67.3% for LightGCN, reflecting sparse exact-item replay labels and the strength of recency as an industrial baseline. The result should be read as offline replay evidence for a bounded auxiliary content-taste feature that preserves the deployability advantage of a request-time-LLM-free serving path.

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Latest papers 126