This is a paper list for pretrained recommend System (recommendation) models. It also contains some related research areas.
Keyword: Recommend System, pretrained models
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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]
- Yelp[link]
- Petdata[link]
- M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining, CVPR 2022
- 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]
- 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]
- 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]
By Xiangyang Li (xiangyangli@pku.edu.cn) from Peking University.