Librerías para motores de recomendación
For una librería para crear sistemas de recomendación, the strongest matches are lyst/lightfm (LightFM is a Python library that builds hybrid recommendation), recommenders-team/recommenders (This repository is a comprehensive Python framework purpose-built for) and microsoft/recommenders (Microsoft Recommenders is a Python framework that provides complete). nicolashug/surprise and apple/turicreate round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
Frameworks y algoritmos open-source para crear sistemas de recomendación de contenido personalizados y herramientas de modelado predictivo de usuarios.
Librerías para motores de recomendación
Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
- lyst/lightfm
lyst/lightfm
5,095Ver en GitHubLightFM 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
LightFM is a Python library that builds hybrid recommendation systems combining collaborative and content-based filtering via matrix factorization, explicitly handles implicit feedback, and includes evaluation and pipeline integration tools — making it a comprehensive fit for this search.
PythonHybrid Matrix FactorizationImplicit Feedback ModelingImplicit Feedback Optimization - recommenders-team/recommenders
recommenders-team/recommenders
21,769Ver en GitHubThis project is a recommendation system framework designed for building, evaluating, and operationalizing personalized item suggestion engines. It provides a comprehensive toolkit for implementing collaborative filtering and content-based algorithms, supported by an end-to-end machine learning pipeline for preparing datasets and deploying predictive models. The framework distinguishes itself through the integration of knowledge graphs to provide richer context for recommendations and the use of industry-specific patterns to accelerate system deployment. It also includes a specialized model ev
This repository is a comprehensive Python framework purpose-built for building, evaluating, and deploying personalized recommendation engines, covering collaborative filtering, content-based algorithms, evaluation metrics, and pipeline integration — exactly what the visitor needs.
PythonCollaborative Filtering ModelsContent-Based FilteringInteraction Matrix Factorizers - microsoft/recommenders
Microsoft/Recommenders
21,771Ver en GitHubRecommenders is a recommendation system framework designed for building, benchmarking, and deploying collaborative and content-based filtering models. It provides a machine learning model pipeline that standardizes the process of moving recommendation data from raw ingestion through training and evaluation. The project functions as a model benchmarking toolkit, utilizing standardized ranking and error metrics to compare the accuracy of different algorithms. It also serves as a hyperparameter tuning tool, allowing for the optimization of model behavior and performance via external configuratio
Microsoft Recommenders is a Python framework that provides complete pipelines for building, benchmarking, and deploying collaborative and content-based filtering models, including evaluation metrics and data pipeline integration, making it a strong fit for building personalized recommendation systems.
PythonCollaborative Filtering ModelsContent-Based Filtering - nicolashug/surprise
NicolasHug/Surprise
6,793Ver en GitHubSurprise is a Python library for building and analyzing recommendation systems. It provides a comprehensive toolkit for implementing collaborative filtering to predict user preferences and generate item suggestions based on historical rating patterns. The library includes dedicated tools for hyperparameter optimization and model evaluation. It allows for searching through parameter sets to find the most effective configurations and utilizes a suite of metrics to measure prediction accuracy. The framework covers the full development workflow, including data loading from various sources, the c
Surprise is a dedicated Python library for building and evaluating collaborative filtering recommendation systems, covering matrix factorization (SVD), cross-validation, hyperparameter tuning, and a wide range of accuracy metrics, making it an excellent fit for this search.
PythonCollaborative Filtering ModelsModel Evaluation Metrics - apple/turicreate
apple/turicreate
11,171Ver en GitHubThis project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit
Turi Create is a Python-compatible machine learning toolkit that provides built-in recommender models (including collaborative filtering, matrix factorization, and implicit feedback) along with evaluation metrics and data pipeline integration, directly addressing the need for a recommendation system library.
C++Model Exporters - tensorflow/recommenders
tensorflow/recommenders
2,023Ver en GitHubTensorFlow Recommenders is a library for building recommender system models using TensorFlow.
TensorFlow Recommenders is a dedicated library for building recommender system models, supporting collaborative filtering, matrix factorization, implicit feedback, and evaluation metrics, with seamless integration into TensorFlow data pipelines and model export, exactly matching your need for a personalization engine.
PythonDeep Learning FrameworksRecommender Systems - gorse-io/gorse
gorse-io/gorse
9,717Ver en GitHubGorse is a personalized recommendation engine server and machine learning pipeline designed to suggest items to users based on their behavior and preferences. It operates as a distributed system that separates training, candidate generation, and serving nodes to support high-throughput workloads. The system utilizes a multi-stage recommendation pipeline to refine results through retrieval, scoring, and reranking. It generates personalized suggestions using collaborative filtering, matrix factorization, and item-to-item similarity models, while also providing non-personalized and fallback reco
Gorse is a full-featured recommendation engine server that uses collaborative filtering, matrix factorization, and item-to-item similarity to personalize suggestions, and it provides a Python SDK for integration—so while it is a deployed service rather than a Python importable library, it squarely delivers the recommendation system / personalization engine the visitor is after.
GoCollaborative Filtering ModelsInteraction Matrix FactorizersModel Evaluation Metrics - benfred/implicit
benfred/implicit
3,797Ver en GitHubImplicit 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
benfred/implicit is a Python library dedicated to fast collaborative filtering and matrix factorization for implicit feedback datasets, directly serving the core of building recommendation systems, though it does not include content-based filtering or built-in evaluation metrics.
PythonImplicit Feedback ModelingInteraction Matrix Factorizers - alibaba/easyrec
alibaba/EasyRec
2,335Ver en GitHubA framework for large scale recommendation algorithms.
EasyRec is a Python framework for large-scale recommendation algorithms, supporting deep learning models like DeepFM and DIN, which fits your search for a recommendation system library even if it emphasizes neural approaches over classical matrix factorization or built-in evaluation metrics.
PythonRecommender FrameworksRecommender Systems - davidcelis/recommendable
davidcelis/recommendable
1,354Ver en GitHubRecommendable is a Ruby library designed to integrate recommendation engines directly into database-backed applications. It provides a framework for tracking user interactions, such as likes, dislikes, and bookmarks, to build detailed interest profiles and generate personalized content suggestions. The engine distinguishes itself by utilizing collaborative filtering to identify relationships between items based on overlapping user behavior. It supports both personalized suggestions tailored to individual preferences and aggregate popularity rankings that surface trending content across the en
This is a recommendation engine for Ruby apps that uses explicit likes/dislikes to generate recommendations, so it fits the category of recommendation system library, though it is Ruby-specific and focuses on explicit feedback rather than covering the broader set of collaborative filtering, matrix factorization, or implicit feedback features you might be looking for in a Python library.
RubyPersonalized Recommendation RetrievalCollaborative Filtering SystemsInteraction Tracking Systems - shenweichen/deepctr
shenweichen/DeepCTR
8,039Ver en GitHubDeepCTR is a specialized software framework and deep learning model library designed for predicting click-through rates and implementing recommendation systems. It provides a suite of tabular data models and architectures tailored for binary classification and sparse feature processing. The framework includes dedicated toolkits for multi-task learning and sequential interest modeling. It allows for the simultaneous estimation of multiple related targets through shared-bottom and gated expert neural networks, while capturing evolving user behavior using attention mechanisms and transformers.
DeepCTR is a Python deep-learning library for click-through rate prediction and recommendation models, which directly supports building personalized recommendation systems — though it specializes in deep CTR models rather than covering every traditional feature like explicit collaborative filtering or evaluation metrics such as NDCG out of the box.
PythonClick-Through Rate PredictorsDeep Learning LibrariesFeature Interaction Models - bytedance/monolith
bytedance/monolith
9,271Ver en GitHubMonolith is a distributed recommendation model framework and asynchronous training engine designed to build and train large-scale deep learning architectures. It functions as a distributed model trainer that processes massive datasets across multiple compute nodes using asynchronous update mechanisms. The system features a dedicated embedding table manager that creates unique, feature-isolated tables to prevent representation collisions. It also includes a real-time weight updater to capture immediate changes in user interest and data hotspots through continuous parameter synchronization. Th
Monolith is a distributed deep learning framework specifically designed for building recommendation models, making it the right kind of tool, though it focuses on deep learning architectures rather than the classical collaborative filtering or SVD algorithms you listed.
PythonRecommendation ArchitecturesRecommendation ModelsAsynchronous Training
