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Collections of pre-implemented recommendation algorithms for rapid testing and benchmarking.
Distinct from Algorithm Implementations: Specializes general algorithm implementations specifically for recommendation system models.
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RecBole is a PyTorch-based recommendation framework designed for building, training, and evaluating a wide variety of recommendation algorithms. It serves as a standardized benchmark environment that allows for the comparison of different model architectures using public datasets and consistent evaluation metrics. The project provides specialized toolkits for sequential recommendation and knowledge-graph integration, enabling the prediction of item sequences based on user history or the incorporation of structured external knowledge. It includes a dedicated hyperparameter optimization engine
Includes a comprehensive library of pre-implemented general and context-aware recommendation models.