Open-source alternatives to Lightfm

This 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

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

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

This is a PyTorch recommendation framework and deep learning recommendation model designed to generate personalized content predictions. It functions as a distributed embedding trainer that processes dense and sparse features through a neural network architecture to predict user preferences. The project implements a CUDA-optimized machine learning system using specialized GPU kernels to accelerate embedding lookup and aggregation. It employs a distributed approach to shard massive sparse feature tables across multiple GPUs, enabling the training of large-scale models. The system utilizes a t

River is a Python framework for online machine learning, designed to train and evaluate models on streaming data. It enables incremental learning by updating model parameters one observation at a time, eliminating the need to store full training datasets in memory. The library distinguishes itself through a dedicated concept drift detection system that monitors changes in data distributions to trigger model adaptation. It also provides a progressive validation framework that simulates real-time deployment by testing models on samples before using them for training. The system covers a broad

This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ

The algorithm-ml is a machine learning ranking engine designed to personalize content feeds by calculating relevance scores for items based on user interests and historical interaction data. It functions as a recommendation system that processes user behavior and item metadata to determine the optimal order of content for individual users. The system utilizes a multi-stage ranking architecture that filters large pools of candidate items into smaller sets before applying computationally expensive scoring models. It employs gradient-boosted decision tree ensembles to capture non-linear relation

fun-rec is a learning guide and framework for building personalized recommendation systems, covering everything from deep learning ranking to generative recommendation paradigms. It provides instructional content on constructing industrial-grade architectures that span offline data processing and real-time online serving. The project distinguishes itself by focusing on generative recommendation, treating the suggestion process as a sequence-to-sequence task using large language models and transformer models to generate item identifiers rather than traditional ranking lists. It also emphasizes

Bustag is a containerized media library manager and automated metadata aggregator. It serves as a centralized database for tracking digital assets, utilizing machine learning models to act as a predictive content filter that identifies and recommends media based on user preferences. The system distinguishes itself through a supervised preference modeling workflow, where users manually label assets as liked or disliked to train predictive models. These models then automate content curation and personalized recommendations by analyzing labeled datasets. The platform integrates web data aggrega

PaddleRec is a deep learning recommendation library and distributed model training framework based on the PaddlePaddle framework. It provides a suite of industrial-scale algorithms and models for user matching and personalized content ranking. The project includes a recommendation inference engine for exporting and serving trained models to production environments for real-time online requests. It enables the implementation of deep learning recommendation algorithms for processing massive behavioral datasets. The framework covers large-scale model training across distributed computing cluste

DeepCTR 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.

This repository is a collection of implementation references and solved notebooks covering supervised, unsupervised, and reinforcement learning techniques. It provides practical guides for building predictive models, clustering algorithms, and autonomous agents. The project includes specific implementations for neural network architectures, such as multi-layer perceptrons for digit recognition, and recommender systems using collaborative and content-based filtering. It also features reinforcement learning systems that utilize deep Q-learning to optimize decision-making policies. The codebase

This project is a PyTorch person re-identification framework designed for training and evaluating models that identify individuals across different camera views. It provides a complete model training pipeline, a deep learning feature extractor for converting images into numeric vectors, and a suite of computer vision benchmarking tools to measure identity retrieval accuracy. The framework includes a specialized transfer learning toolkit that supports layer freezing, staged learning rate optimization, and differential learning rates for fine-tuning pretrained models. It distinguishes itself th

This project is a diffusion model training framework and image synthesis pipeline. It provides the tools necessary to train generative models to learn image data distributions through an iterative denoising process. The framework includes a generative model evaluation tool consisting of automated scripts used to measure the quality and accuracy of produced samples. The system covers model training pipelines and performance evaluation for generative diffusion models.

A framework for large scale recommendation algorithms.

Deep recommender models using PyTorch.

This project is a generative adversarial network implementation and research framework. It provides the tools and hyperparameters necessary to train and evaluate generative models across various datasets, specifically designed to reproduce results from academic research. The framework includes a Parzen density likelihood estimator to calculate model log likelihood. This allows for the quantitative evaluation of generative distributions and the measurement of overall model performance. The codebase covers machine learning research capabilities, focusing on the training of adversarial networks

This 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

This project is a deep learning tutorial series and educational curriculum designed to teach PyTorch fundamentals. It serves as a structured training guide for mastering neural network architecture, automatic differentiation, and the use of tensors and dynamic computation graphs. The curriculum focuses on practical implementations, specifically guiding the development of recommendation systems, advertising models, and interest networks to predict user preferences. It also provides instructional content for time series forecasting and processing sequential data. The material covers a broad ra

This repository is a collection of practical deep learning implementations and examples built using the TensorFlow framework. It provides a variety of neural network architectures focusing on natural language processing, recommendation systems, reinforcement learning, and time series prediction. The project features a range of specialized models, including sequence-to-sequence and transformer architectures for text processing, and factorization machines for personalized ranking and retrieval. It also includes implementations of reinforcement learning agents using actor-critic and policy gradi

This project is an educational collection of tutorials and executable code notebooks focused on data science, machine learning, deep learning, and natural language processing concepts in Python. It provides instructional resources covering statistical analysis, linear algebra, artificial intelligence algorithms, and step-by-step guides for developers learning data science. The repository covers a broad spectrum of computational and statistical capabilities, including neural network construction, gradient-based optimization techniques, curve fitting, regression modeling, and collaborative filt

X-algorithm is a modular recommendation engine framework designed to orchestrate personalized content feeds. It functions as a machine learning ranking system that manages the end-to-end lifecycle of content delivery, from initial candidate retrieval to final display ordering. The system distinguishes itself through a multi-stage pipeline that integrates vector-based similarity search with transformer-based engagement prediction. By mapping user history and content features into high-dimensional embeddings, it performs rapid approximate nearest neighbor searches to identify relevant items. Th

This project is an end-to-end recommendation pipeline and framework designed for building generative recommendation engines. It utilizes large language models and vector search to facilitate personalized item discovery and produces item recommendations through tokenization and semantic alignment. The system integrates a generative recommendation engine with a multi-objective ranking system and a vector search retrieval pipeline. These components allow for the use of reasoning frameworks to perform inference over items and the application of re-ranking algorithms to optimize the diversity of t

Neural 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

Bangumi is a cross-platform mobile application that serves as an anime and manga tracker, a community client for BGM.tv, and a local media library manager. It enables users to record viewing progress, browse community-driven media databases, and link local media files to official database entries through pattern-based filename scraping. The application functions as a media discovery tool, utilizing AI recommendations, relationship graphs, and community trend analysis to find new content. It integrates a social ecosystem that allows users to participate in forums, manage friendships, and brows

This project is a collection of foundational machine learning algorithms and data science tools implemented in Python. It focuses on building the logic of these tools using basic programming primitives rather than relying on specialized libraries. The implementation covers several core domains, including a linear algebra library for matrix and vector operations, a statistical analysis toolkit for probability and hypothesis testing, and a framework for map-reduce distributed processing. It also includes implementations for natural language processing, graph theory for network analysis, and var