This repository contains a curated list of papers on cold-start user/item recommendations, which are sorted by their published years.
Continuously updating!
(WWW 2024) Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendation [Paper] [Code]
(AAAI 2024) Temporally and Distributionally Robust Optimization for Cold-start Recommendation [Paper] [Code]
(AAAI 2024) Preference Aware Dual Contrastive Learning for Item Cold-Start Recommendation [Paper]
(ICASSP 2024) Mutual Information Assisted Graph Convolution Network for Cold-Start Recommendation [Paper]
(WSDM 2024) CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process [Paper]
(WSDM 2024) Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation [Paper] [Code]
(T-Recsys 2024) Cold-Start Recommendation based on Knowledge Graph and Meta-Learning under Positive and Negative sampling [Paper] [Code]
(WWW 2024 Companion) Large Language Models as Data Augmenters for Cold-Start Item Recommendation [Paper]
(Arxiv 2024) Large Language Model Interaction Simulator for Cold-Start Item Recommendation [Paper]
(Arxiv 2024) Keyword-driven Retrieval-Augmented Large Language Models for Cold-start User Recommendations [Paper]
(SIGIR 2023) Aligning Distillation For Cold-start Item Recommendation [Paper] [Code]
(SIGIR 2023) A Preference Learning Decoupling Framework for User Cold-Start Recommendation [Paper]
(SIGIR 2023) M2EU: Meta Learning for Cold-start Recommendation via Enhancing User Preference Estimation [Paper] [Code]
(SIGIR 2023) Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation [Paper]
(WWW 2023) Contrastive Collaborative Filtering for Cold-Start Item Recommendation [Paper] [Code]
(WWW 2023) ColdNAS: Search to Modulate for User Cold-Start Recommendation [Paper] [Code]
(ICDE 2023) Automatic Fusion Network for Cold-start CVR Prediction with Explicit Multi-Level Representation [Paper] [Code]
(MM 2023) GoRec: A Generative Cold-Start Recommendation Framework [Paper] [Code]
(WSDM 2023) Meta Policy Learning for Cold-Start Conversational Recommendation [Paper] [Code]
(CIKM 2023) An Unified Search and Recommendation Foundation Model for Cold-Start Scenario [Paper]
(CIKM 2023) Modeling Preference as Weighted Distribution over Functions for User Cold-start Recommendation [Paper] [Code]
(CIKM 2023) Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation [Paper] [Code]
(CIKM 2023) Self-supervised Contrastive Enhancement with Symmetric Few-shot Learning Towers for Cold-start News Recommendation [Paper]
(CIKM 2023) Task-Difficulty-Aware Meta-Learning with Adaptive Update Strategies for User Cold-Start Recommendation [Paper]
(CIKM 2023) Boosting Meta-Learning Cold-Start Recommendation with Graph Neural Network [Paper]
(RecSys 2023) Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences [Paper]
(IJCNN 2023) Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation [Paper]
(TKDE 2023) Contrastive Proxy Kernel Stein Path Alignment for Cross-Domain Cold-Start Recommendation [Paper]
(TOIS 2023) User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural Network [Paper]
(TOIS 2023) A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation [Paper]
(TORS 2023) User Cold-start Problem in Multi-armed Bandits: When the First Recommendations Guide the User’s Experience [Paper]
(KDD 2022) Task-optimized User Clustering based on Mobile App Usage for Cold-start Recommendations [Paper]
(SIGIR 2022) Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation [Paper]
(SIGIR 2022) Socially-aware Dual Contrastive Learning for Cold-Start Recommendation [Paper]
(SIGIR 2022) Generative Adversarial Framework for Cold-Start Item Recommendation [Paper] [Code]
(SIGIR 2022) Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder [Paper] [Code]
(WWW 2022) Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework [Paper]
(WWW 2022) PNMTA: A Pretrained Network Modulation and Task Adaptation Approach for User Cold-Start Recommendation [Paper]
(ICDE 2022) Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck [Paper] [Code]
(AAAI 2022) A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations [Paper] [Code]
(AAAI 2022) SMINet: State-Aware Multi-Aspect Interests Representation Network for Cold-Start Users Recommendation [Paper] [Code]
(CIKM 2022) Generative Adversarial Zero-Shot Learning for Cold-Start News Recommendation [Paper]
(CIKM 2022) GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction [Paper]
(CIKM 2022) Addressing Cold Start in Product Search via Empirical Bayes [Paper]
(CIKM 2022) Multimodal Meta-Learning for Cold-Start Sequential Recommendation [Paper] [Code]
(CIKM 2022) Task Similarity Aware Meta Learning for Cold-Start Recommendation [Paper]
(CIKM 2022) Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN [Paper]
(KDD 2021) Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation [Paper] [Code]
(KDD 2021) A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps [Paper] [Code]
(SIGIR 2021) Sequential Recommendation for Cold-start Users with Meta Transitional Learning [Paper] [Code]
(SIGIR 2021) Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks [Paper]
(SIGIR 2021) Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction [Paper] [Code]
(SIGIR 2021) FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation [Paper]
(SIGIR 2021) Privileged Graph Distillation for Cold Start Recommendation [Paper]
(SIGIR 2021) Fairness among New Items in Cold Start Recommender Systems [Paper] [Code]
(SIGIR 2021) Cluster-Based Bandits: Fast Cold-Start for Recommender System New Users [Paper]
(SIGIR 2021) Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users [Paper]
(WWW 2021) Task-adaptive Neural Process for User Cold-Start Recommendation [Paper] [Code]
(AAAI 2021) Cold-start Sequential Recommendation via Meta Learner [Paper]
(AAAI 2021) Personalized Adaptive Meta Learning for Cold-start User Preference Prediction [Paper]
(IJCAI 2021) Preference-Adaptive Meta-Learning for Cold-Start Recommendation [Paper] [Code]
(MM 2021) Contrastive Learning for Cold-Start Recommendation [Paper] [Code]
(CIKM 2021) Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation [Paper]
(CIKM 2021) Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems [Paper] [Code]
(CIKM 2021) CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation [Paper]
(KDD 2020) MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation [Paper] [Code]
(KDD 2020) Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation [Paper] [Code]
(SIGIR 2020) Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation [Paper] [Code]
(SIGIR 2020) CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network [Paper] [Code]
(SIGIR 2020) Content-aware Neural Hashing for Cold-start Recommendation [Paper] [Code]
(SIGIR 2020) A Heterogeneous Graph Neural Model for Cold-start Recommendation [Paper]
(SIGIR 2020) DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain [Paper]
(AAAI 2020) Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation [Paper]
(IJCAI 2020) Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation [Paper] [Code]
(MM 2020) How to Learn Item Representation for Cold-Start Multimedia Recommendation? [Paper] [Code]
(CIKM 2020) Dual Autoencoder Network with Swap Reconstruction for Cold-Start Recommendation [Paper] [Code]
(CIKM 2020) Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval [Paper]
(TKDE 2020) Attribute Graph Neural Networks for Strict Cold Start Recommendation [Paper]
(TKDE 2020) MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation [Paper] [Code]
(KDD 2019) MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation [Paper] [Code]
(SIGIR 2019) Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings [Paper] [Code]
(AAAI 2019) From Zero-Shot Learning to Cold-Start Recommendation [Paper] [Code]
(IJCNN 2019) Meta-Learning for User Cold-Start Recommendation [Paper]
(NIPS 2017) DropoutNet: Addressing Cold Start in Recommender Systems [Paper] [Code]
(NIPS 2013) Deep Content-based Music Recommendation [Paper]
(SIGIR 2001) Generative Models for Cold-Start Recommendations [Paper]
ColdRec: An Open-Source Benchmark Toolbox for Cold-Start Recommendation [Github]
