2 repositorios
Awesome GitHub RepositoriesRecommendation Benchmarks
Standardized environments and datasets used to compare the performance of recommendation algorithms.
Distinguishing note: Focuses on the environment for comparison rather than just the dataset or the model.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Recommendation Benchmarks. Refine with filters or upvote what's useful.
Awesome Recommendation Benchmarks GitHub Repositories
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- rucaibox/recbole
RUCAIBox/RecBole
4,487Ver en GitHubRecBole 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
Offers a standardized environment for comparing recommendation models using public datasets and consistent evaluation metrics.
Python - hexiangnan/neural_collaborative_filtering
hexiangnan/neural_collaborative_filtering
1,885Ver en GitHubNeural collaborative filtering is a recommendation system framework that predicts user item preferences from implicit feedback by combining generalized matrix factorization and multi-layer perceptron networks through a shared final embedding layer. It captures both linear and non-linear interactions to model user preferences from historical data. The framework executes training and evaluation runs through a configuration-driven pipeline accessible via command-line interfaces, parsing hyperparameters such as learning rates, batch sizes, and latent dimensions. It optimizes implicit feedback mod
Measures the ranking quality of item recommendations using benchmark datasets with positive interactions and negative samples.
Pythoncollaborative-filteringdeep-learningrecommender-system
