Large Language Model-Based Recommendation

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 126

All topics

Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation
Jun 29, 2026Zhe Dong, Fang Qin, Manish Shah +1Cold-Start Recommendation LLM Reranking
University of Maine at Presque Isle · Stanford University · 2Stanford University +2
Reproducing FACTER: Fairness via Conformal Thresholding and Prompt Repair
Jun 26, 2026Oscar Miró López-Feliu, Daimy van Loo, Xanthos Kekkos +2Algorithmic Fairness Conformal Prediction
University of Amsterdam
Who Owns the AI Recommendation? A Multi-Industry Empirical Map of Brand Category Ownership Across Large Language Models
Jun 22, 2026Dmitrij ŻatuchinLLM Evaluation LLM Auditing
Department of Information Technologies, Estonian Entrepreneurship University of Applied Sciences (EUAS), Tallinn, Estonia. · Rankfor.AI, Tallinn, Estonia.
Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models
Jun 17, 2026Xilun Chen, Shao-Chuan Wang, Baykal Cakici +6Recommender Systems Large Language Model-Based Recommendation
Google · USA
Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
Jun 16, 2026Xi Chu, Yupeng HouGenerative Engine Optimization Language Model Bias Evaluation
Trine University · Texas A&M University
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.
Whose hotel does the AI recommend? An algorithm audit of reputation signals in LLM-assisted hotel selection
Jun 15, 2026Mirza Samad Ahmed Baig, Syeda Anshrah Gillani, Asher AliLLM Auditing Recommender Systems
Fandaqah, Al Khobar, Saudi Arabia. · Hamdard University, Karachi, Pakistan.
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
Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants
Jun 10, 2026Shuxian Fan, Seonwoo Min, Youna Hu +7Multi-Turn Dialogue Evaluation Large Language Model-Based Recommendation
Amazon
Generative Archetype-Grounded Item Representations for Sequential Recommendation
Jun 9, 2026Yifan Li, Jiahong Liu, Xinni Zhang +5Sequential Recommendation Representation Learning
The Chinese University of Hong Kong Hong Kong SAR, China · McGill University Montreal, Canada · Tongji University Shanghai, China
Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
Jun 9, 2026Zhuohang Jiang, Yuxin Chen, Shijie Wang +6LLM Inference Acceleration User Modeling
The Hong Kong Polytechnic University Hong Kong SAR, China · Kuaishou Technology Beijing, China
ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation
Jun 8, 2026Yihua Zhang, Mingfu Liang, Jiyan Yang +11Efficient Multimodal Inference Multimodal CoT Reasoning
Meta AI · Michigan State University · The University of North Carolina at Chapel Hill +1
SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents
Jun 8, 2026Qianfeng Wen, Yifan Simon Liu, Xin Liu +4Generative Engine Optimization LLM Agent Security
Department of Computer Science, University of Toronto · Department of Mechanical & Industrial Engineering, University of Toronto · Faculty of Information, University of Toronto +3
The Injection Paradox: Brand-Level Suppression in Safety-Trained LLM Recommendations via RAG Context Injection
Jun 8, 2026Hyunseok PaengRetrieval-Augmented Generation Adversarial Attacks on LLMs
Independent Researcher.
Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
Jun 6, 2026Qingxiang Liu, Anqi Liang, Zhuoyang Jiang +5Sequential Recommendation User Modeling
The Hong Kong University of Science and Technology (Guangzhou) China · Shanghai Jiao Tong University China · The Hong Kong University of Science and Technology Hong Kong +2
Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations
Jun 4, 2026Nimesh Sinha, Raghav Saboo, Martin Wang +1Cold-Start Recommendation Recommender Systems
DoorDash Inc. San Francisco, California, USA
The Geography of Algorithmic Judgment: LLM Intermediaries, Place Identity, and Racial Steering in Housing Search
Jun 4, 2026Hana Samad, Trung Lam, Christoph Mügge-Durum +1LLM Auditing Social Bias in Language Models
Responsible AI Lab · Chief AI Officer and Head of the Responsible AI Lab, National Fair Housing Alliance, Washington DC, USA
Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation
Jun 2, 2026Yuecheng Li, Zeyu Song, Jing Yao +3RL for Language Models Large Language Model-Based Recommendation
Kuaishou Technology Beijing, China · Unaffiliated Beijing, China
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
Jun 1, 2026Amir Ghasemian, Homa Hosseinmardi, Upasana Dutta +1LLM Prompting LLM Reranking
Department of Communication, University of California, Los Angeles, CA 90095 · Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104 · Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA 19104 +1
Persona Conditioning of Brand Recommendations in Retrieval-Augmented Commercial Chat: A Prominence-Stratified Cross-Provider Audit
May 28, 2026Will Jack, Noah Lehman, Keller Maloney +1LLM Personalization Recommender Systems
Unusual
Multimodal Music Recommendation System using LLMs
May 28, 2026Srikar Prabhas Kandagatla, Sreehitha R. Narayana, Chandana Magapu +6Sequential Recommendation Recommender Systems
University of Massachusetts Amherst, Massachusetts, USA · Dolby Laboratories Atlanta, Georgia, USA · Adobe Research San Jose, California, USA +2
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
Whose Name Comes Up? III: Persona Prompting Effects in LLM-Based Scholar Recommendation
May 27, 2026Annabella Sánchez-Guzmán, Lukas Eberhard, Denis Helic +1LLM Auditing LLM Prompting
Escuela Superior Polit´ecnica del Litoral · Graz University of Technology · Complexity Science Hub
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
Fine-Tuned LLM as a Complementary Predictor Improving Ads System
May 27, 2026Hui Yang, Daiwei He, Kevin Jiang +20LLM Fine-Tuning Online Advertising
Pinterest, Inc., USA
L2Rec: Towards Dual-View Understanding of LLMs for Personalized Recommendation
May 26, 2026Pingjun Pan, Tingting Zhou, Peiyao Lu +3Mixture-of-Experts Language Models LLM Personalization
Netease Cloud Music Hangzhou, China
Paraphrase Brittleness in Production Retrieval-Augmented Commercial Recommendation: Reproducibility Below the Rerun-Stability Baseline
May 22, 2026Will Jack, Noah Lehman, Keller Maloney +1Prompt Sensitivity LLM Evaluation
Unusual
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
Prominence-Stratified Failure Modes in Retrieval-Augmented Commercial Recommendation: A 37,000-Run Audit
May 22, 2026Will Jack, Noah Lehman, Keller Maloney +1Generative Engine Optimization Recommender Systems
Unusual

Jun 29, 2026, cs.IRJ/K move · Enter open · S save

Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation

Zhe Dong, Fang Qin, Manish Shah, Yicheng Wang

University of Maine at Presque Isle · Stanford University · 2Stanford University+2

Cold-Start Recommendation LLM Reranking Recommender Systems Large Language Model-Based Recommendation Hybrid Retrieval

Open paper AskPDF arXiv

Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes. We test this assumption with a five-domain benchmark that explicitly separates reranking quality from retrieval coverage. In a positive-controlled regime where the gold item is guaranteed present, calibrated LLM rerankers fail to consistently outperform strong collaborative and content baselines under natural traffic, and within-family scaling from Qwen3-8B to Qwen3-32B narrows but does not close the gap on most domains. In a retrieval-realistic regime where the gold item is not injected, the bottleneck is more severe: standard single retrievers place the gold item in a 200-item pool only 4.6-22.9% of the time, largely because 32-91% of cold-start targets are brand-new items with no training interactions. We introduce LHF, a validation-trained learned hybrid fusion layer over a multi-retriever union pool, as a retrieval-side realizability baseline. LHF is the only combiner we test that beats every single retriever on all five domains and recovers 17-61% of oracle coverage headroom on content-rich domains, but only 5-7% on collaboratively strong domains. End-to-end experiments reveal the remaining mismatch: learned non-LLM ranking exploits the LHF pool, while prompt-level LLM reranking often degrades it. LLMs exhibit pockets of semantic cold-start advantage, especially in text-rich domains when the item is already present, but this advantage is largely unreachable in current retrieve-then-rerank pipelines. We release the benchmark protocol, splits, prompts, evaluation tooling, and archived reproducibility artifacts: data at https://doi.org/10.5281/zenodo.20991039 and code at https://doi.org/10.5281/zenodo.20993306.

Momentum

Related topics

Latest papers 126