Cold-Start Recommendation

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Challenges and Solutions for Bandits in the Wild: Warm-Started Mixture Bandits for Cross-Cohort Slate Recommendation
Sep 29, 2026Serafima Lebedeva, Sumantrak Mukherjee, Ali Arshad Sadal +8Multi-Armed Bandits Cold-Start Recommendation
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern · RPTU, Kaiserslautern-Landau · LMU, Munich
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
LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendation
Aug 4, 2026Eugene Lee, Oseong Choi, Byungsoo Kang +1Thompson Sampling Cold-Start Recommendation
NAVER WEBTOON South Korea
Kairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB
Jul 29, 2026Finn HertschCold-Start Recommendation Recommender Systems
DHBW Ravensburg, School of Business, Data Science and AI, Marienplatz 2, 88212 Ravensburg, Germany
PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest
Jul 24, 2026Olafur Gudmundsson, Bo Zhao, Huayi Liao +15Cold-Start Recommendation Information Retrieval
Pinterest, Inc. San Francisco, USA
Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation
Jul 23, 2026Jie Peng, Yanping Zheng, Zhewei Zhe +3Semantic ID Recommendation Sequential Recommendation
Renmin University of China · Beijing, China · Alibaba
Bridging the Information Gap: Semantic Densification and Hindsight Distillation for Cold-Start Prediction
Jul 19, 2026Hao Duong Le, Yifei Gao, Huan Li +4Cold-Start Recommendation User Modeling
Tsinghua University Beijing, China · Meituan Beijing, China
ViHoRec: A Quality-Controlled Vietnamese Hotel Recommendation Dataset and Cold-Start Benchmark
Jul 14, 2026Minh Hoang NguyenCold-Start Recommendation Recommender Systems
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam
Proximity Features: Privacy-Compliant Cold-Start Personalization at Airbnb
Jul 14, 2026Wei Jiang, Bin Xu, Hui Gao +6Cold-Start Recommendation Privacy-Preserving ML
Airbnb Inc. San Francisco, USA
SISA-Rec: A Semantically Integrated Sequential Recommender with Contrastive Alignment
Jul 13, 2026Soohan Abbasi, Shahid Munir Shah, Rafia Shaikh +1Sequential Recommendation Cold-Start Recommendation
Department of Computer Science, DHA Suffa University, Karachi, Sindh, Pakistan · Department of Computer Science, SZABIST, Pakistan · National University of Computer and Emerging Sciences (FAST), Karachi, Sindh, Pakistan +1
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
Denoising Implicit Feedback for Cold-start Recommendation
Jun 17, 2026Gaode Chen, Shicheng Wang, Shikun Li +8Cold-Start Recommendation Noisy-Label Learning
Kuaishou Technology Beijing, China · Hong Kong Baptist University Hong Kong, China · Institute of Information Engineering, Chinese Academy of Sciences Beijing, China
VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions
Jun 17, 2026Katya Mirylenka, Egor Malykh, Mahdyar Ravanbakhsh +10Multimodal IR Cold-Start Recommendation
TU Wien, Vienna, Austria · Zalando Switzerland AG, Zurich, Switzerland · Zalando SE, Berlin, Germany
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
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
Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation
Jun 4, 2026Anh Truong, John Trenkle, Yuanbo Chen +4Cold-Start Recommendation Graph Representation Learning
Tubi San Francisco, USA · Kumo AI San Francisco, USA
Representation Curriculum: Stagewise Training for Robust Ranking and Allocation
Jun 3, 2026Ehsan Ebrahimzadeh, Sina Baharlouei, Abraham BagherjeiranCold-Start Recommendation Representation Learning
eBay Search Ranking and Monetization San Jose, California, USA
FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation
May 20, 2026Xinhang Yuan, Zexi Huang, Anjia Cao +6Semantic ID Recommendation Cold-Start Recommendation
TikTok · ByteDance
Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search
May 18, 2026Yifan Wang, Yixuan Wang, YiDan Liang +2Cold-Start Recommendation E-Commerce Search
Taobao & Tmall Group of Alibaba · HangZhou, China
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

Sep 29, 2026, cs.LGJ/K move · Enter open · S save

Challenges and Solutions for Bandits in the Wild: Warm-Started Mixture Bandits for Cross-Cohort Slate Recommendation

Serafima Lebedeva, Sumantrak Mukherjee, Ali Arshad Sadal, Ilias Ekşi+7

German Research Center for Artificial Intelligence (DFKI), Kaiserslautern · RPTU, Kaiserslautern-Landau · LMU, Munich

Multi-Armed Bandits Cold-Start Recommendation Recommender Systems Combinatorial Multi-Armed Bandits

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Many recommender services repeatedly encounter cold-start cohorts, where new users arrive with little or no interaction history. This creates two challenges: learning user preferences quickly from limited feedback and sustaining useful recommendations when each user has a finite catalog that can become repetitive or depleted over time. We propose CohortMix-TS, a warm-started mixture bandit that learns latent user groups from earlier cohorts and uses available metadata to construct group-informed priors for new users. Starting from these fixed priors, the model personalizes independently as feedback from each user becomes available. Session slates combine Thompson sampling with diversity and inventory-depletion controls. We evaluate CohortMix-TS through simulation, semi-synthetic experiments, and a 25-day randomized in-the-wild deployment with 713 registered participants in a Campus Games quiz application. Our evaluations show that cross-cohort transfer improves early recommendation quality and user-level regret, while inventory-aware slate construction helps prevent premature exhaustion of preferred items. In the field deployment, treatment users also showed a larger early-to-late change in correctness than users receiving random recommendations. Together, these results show how warm-start transfer and inventory-aware recommendations can support personalization for short-lived, repeatedly cold-starting cohorts.

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