BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
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Summary
A sequential recommendation model called BERT4Rec is proposed, which employs the deep bidirectional self-attention to model user behavior sequences, and outperforms various state-of-the-art sequential models consistently.
- Type
- preprint
- Published
- 2019-04-14
- Cited by
- 3,468
- References
- 61
- Access
- Open access
- OpenAlex
- https://openalex.org/W2937556626
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:119181611
Keywords
Computer science, Transformer, Encoder, Sequence (biology), Benchmark (surveying)
References
- Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering
- Efficient Estimation of Word Representations in Vector Space
- AutoRec: Autoencoders Meet Collaborative Filtering
- Distilling the Knowledge in a Neural Network
- Factorization meets the neighborhood: a multifaceted collaborative filtering model
- Image-Based Recommendations on Styles and Substitutes
- Item-based collaborative filtering recommendation algorithms
- Matrix Factorization Techniques for Recommender Systems
- Long Short-Term Memory
- Dropout: a simple way to prevent neural networks from overfitting
- Restricted Boltzmann machines for collaborative filtering
- FISM: factored item similarity models for top-N recommender systems
- Bridging Viterbi and posterior decoding: a generalized risk approach to hidden path inference based on hidden Markov models
- Deep content-based music recommendation
- Probabilistic Matrix Factorization
- Distributed Representations of Words and Phrases and their Compositionality
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- Collaborative Deep Learning for Recommender Systems
- Factorizing personalized Markov chains for next-basket recommendation
- Deep Residual Learning for Image Recognition
Cited by
- Productivity, Portability, Performance: Data-Centric Python
- Deep Learning for Matching in Search and Recommendation
- POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
- Deep Learning-Based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations
- CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
- Concept to code: deep learning for multitask recommendation
- Pre-Trained Bidirectional Temporal Representation for Crowd Flows Prediction in Regular Region
- Conversion Rate Prediction via Post-Click Behaviour Modeling
- BERT4SessRec: Content-Based Video Relevance Prediction with Bidirectional Encoder Representations from Transformer
- Malware Detection on Highly Imbalanced Data through Sequence Modeling
- Behavior sequence transformer for e-commerce recommendation in Alibaba
- Multi-Level Coupling Network for Non-IID Sequential Recommendation
- NLP4REC: The WSDM 2020 Workshop on Natural Language Processing for Recommendations
- Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
- Sequential Recommendations on Board-Game Platforms
- Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
- Learning to Structure Long-term Dependence for Sequential Recommendation
- SSE-PT: Sequential Recommendation Via Personalized Transformer
- Superbloom: Bloom filter meets Transformer
- Extended Factorization Machines for Sequential Recommendation
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