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A framework for large scale recommendation algorithms.
LightFM is a Python recommendation library and machine learning framework designed to predict user preferences. It implements a hybrid recommendation engine that combines collaborative filtering with content filtering by integrating user-item interaction data with descriptive metadata. The system utilizes hybrid matrix factorization to learn latent representations of users and items. It is specifically designed to handle implicit feedback, utilizing specialized loss functions such as Weighted Approximate Rank Pairwise and Bayesian Personalized Ranking to optimize item preferences for datasets
Implicit is a Python recommendation engine and matrix factorization library designed for collaborative filtering. It implements predictive models that analyze implicit feedback to estimate user preferences and generate personalized item recommendations without requiring explicit ratings. The library utilizes native-code execution and multi-core parallelized processing to decompose large interaction matrices into latent factors. It incorporates approximate nearest neighbor indexing to accelerate high-dimensional similarity lookups and reduce recommendation latency. The framework covers prefer
Deep recommender models using PyTorch.
The main features of hidasib/gru4rec are: Recommender Frameworks, Recommender Systems.
Projects with overlapping indexed features include: lyst/lightfm — LightFM is a Python recommendation library and machine learning framework designed to predict user preferences. It… rucaibox/recbole — RecBole is a PyTorch-based recommendation framework designed for building, training, and evaluating a wide variety of… benfred/implicit — Implicit is a Python recommendation engine and matrix factorization library designed for collaborative filtering. It… alibaba/easyrec — A framework for large scale recommendation algorithms. maciejkula/spotlight — Deep recommender models using PyTorch. cnclabs/pronet-core — This project has been moved to https://github.com/cnclabs/smore.