This is a paper list for pretrained recommend System (recommendation) models. It also contains some related research areas such as large language model for recommendation.
Keyword: Recommend System, pretrained models, large language model
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- Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect, arXiv, 2020, [paper]
- Self-Supervised Learning for Recommender Systems: A Survey ,arxiv 2022, [paper]
- Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems, arxiv 2022, [paper]
- How Can Recommender Systems Benefit from Large Language Models: A Survey, arxiv 2023, [paper] [code]
- Yelp[link]
- Petdata[link]
- M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining, CVPR 2022 [paper]
- Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems, NeurIPS 2022 [paper]
- PixelRec: A Image Dataset for Benchmarking Recommender Systems with Raw Pixels [link],arxiv 2023,[paper]
- Netflix: [link]
- Ninerec: A benchmark dataset suite for evaluating transferable recommendation [link], arxiv 2023,[paper]
- A Content-Driven Micro-Video Recommendation Dataset at Scale [link], arxiv 2023,[paper]
- Where to Go Next for Recommender Systems? ID-vs. Modality-based recommender models revisited, SIGIR 2023, [paper] [code]
- Generative Recommendation: Towards Next-generation Recommender Paradigm, arxiv 2023, [paper]
- Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights, WSDM 2024, [paper] [code]
- A Simple Convolutional Generative Network for Next Item Recommendation, WSDM 2018/08, [paper] [code]
- BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer, CIKM 2019 , [paper][code]
- S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization , CIKM-2020 , [paper][code]
- Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation, Recsys 2021 , [paper][code]
- Towards Universal Sequence Representation Learning for Recommender Systems , KDD 2022 , [paper][code]
- Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders, WWW 2023, [paper] [code]
- MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation, ACM MM 2023,[paper] [code]
- Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation, SIGIR 2020 , [paper], [code]
- UPRec: User-Aware Pre-training for Recommender Systems ，submitted TKDE in 2021 , [paper]
- U-BERT: Pre-training user representations for improved recommendation, AAAI 2021, [paper]
- UserBERT: Self-supervised User Representation Learning , arxiv 2021 , [paper]
- One4all User Representation for Recommender Systems in E-commerce , arxiv 2021 , [paper]
- One Person, One Model, One World: Learning Continual User Representation without Forgetting, SIGIR 2021 , [paper]
- Scaling Law for Recommendation Models: Towards General-purpose User Representations , AAAI 2023 , [paper]
- Self-supervised Learning for Large-scale Item Recommendations , CIKM 2021 , [paper]
- TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback , arxiv 2022 , [paper]
- IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System, CIKM 2022 , [paper][code]
- Language Models as Recommender Systems: Evaluations and Limitations , NeurIPS 2021 Workshop ICBINB , [paper]
- CTR-BERT: Cost-effective knowledge distillation for billion-parameter teacher models, [paper]
- Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5) , Recsys 2022 , [paper])
- M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems ,arxiv 2022 , [paper]
- PTab: Using the Pre-trained Language Model for Modeling Tabular Data, arxiv 2022, [paper]
- Prompt Learning for News Recommendation, SIGIR 2023, [paper]
- LLMRec: Large Language Models with Graph Augmentation for Recommendation , WSDM 2024 , [paper], [code], [blog in Chinese]
- Is ChatGPT a Good Recommender A Preliminary Study, arxiv 2023, [paper]
- Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent, arxiv 2023, [paper]
- Uncovering ChatGPT’s Capabilities in Recommender Systems, arxiv 2023, [paper][code]
- Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT, arxiv 2023, [paper]
- Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation, arxiv 2023,[paper] [code]
- TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation, arxiv 2023, [paper]
- PALR: Personalization Aware LLMs for Recommendation, arxiv 2023, [paper]
- Large Language Models are Zero-Shot Rankers for Recommender Systems, arxiv 2023, [paper]
- Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach, arxiv 2023, [paper]
- Leveraging Large Language Models in Conversational Recommender Systems, arxiv 2023, [paper]
- Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models, arxiv 2023, [paper]
- Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights, arxiv 2023, [paper]
- A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems,arxiv 2023, [paper]
- CTRL: Connect Tabular and Language Model for CTR Prediction, arxiv 2023,[paper]
- LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking, PGAI@CIKM 2023,[paper] [code]
- LLM4Vis: Explainable Visualization Recommendation using ChatGPT,EMNLP Industry 2023, paper, code
- Curriculum Pre-Training Heterogeneous Subgraph Transformer for Top-N Recommendation , arxiv 2021 ,[paper]
- Contrastive Pre-Training of GNNs on Heterogeneous Graphs , CIKM 2021 , [paper]
- Self-supervised Graph Learning for Recommendation , SIGIR 2021 , [paper]
- Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation , AAAI 2021 , [paper]
https://github.com/CHIANGEL/Awesome-LLM-for-RecSys
By Xiangyang Li ([email protected]) from Peking University.
@misc{rs-pretrain-papers,
author = {Xiangyang Li},
title = {awesome-recommend-system-pretraining-papers},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/archersama/awesome-recommend-system-pretraining-papers/}}
}