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Transformers4Rec is an advanced recommendation system library that leverages Transformer models for sequential and session-based recommendations. The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners. Traditional recommendation algorithms usually ignore the temporal dynamics and the sequence of interactions when trying to model user behavior. Generally, the next user interaction is related to the sequence of the user's previous choices. In some cases, it might be a repeated purchase or song play. User interests can also suffer from interest drift because preferences can change over time. Those challenges are addressed by the sequential recommendation task.
Features
- Uses Transformer architectures for sequential recommendations
- Optimized for GPU acceleration with NVIDIA hardware
- Supports multi-modal inputs like text, categorical, and numerical features
- Works with TensorFlow and PyTorch frameworks
- Enables real-time and batch recommendation pipelines
- Open-source and scalable for large datasets
Project Samples
