Projects sharing features with Implicit

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

This project is a real-time product recommendation engine built on Apache Flink. It functions as a streaming behavioral analytics pipeline that processes raw logs to derive user interests and product popularity trends. The system utilizes a collaborative filtering engine to compute item similarity via cosine similarity and shared user interaction patterns. It employs a hybrid re-ranking pipeline that combines global popularity lists with personalized user profiles to sort recommended items. The architecture incorporates a wide-column user store using HBase for persistent behavioral records a

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

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

Surprise 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

Annoy is a C++ library designed for approximate nearest neighbor search in high-dimensional vector spaces. It functions as a vector similarity search engine that constructs static, disk-based data structures to facilitate fast lookups. By mapping identifiers to vector data and persisting these structures to disk, the library enables efficient, memory-mapped access to large datasets. The project distinguishes itself through the use of random projection trees and distance-metric-based partitioning, which organize data into hierarchical binary trees to balance search precision against computatio

Deep recommender models using PyTorch.

A framework for large scale recommendation algorithms.

This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen

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

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

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

Recommendable 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

Vald is a distributed, cloud-native search engine designed for high-dimensional vector data. It functions as an approximate nearest neighbor search platform, enabling the identification of similar data points across massive datasets through horizontal scaling and distributed indexing. The system is built for container orchestration environments, utilizing custom resource controllers to automate cluster lifecycle management and infrastructure state. It employs graph-based indexing to perform rapid similarity lookups and supports zero-downtime operations by decoupling index construction from qu

Hora is a vector similarity search library written in Rust designed for efficient approximate nearest neighbor retrieval in high-dimensional data spaces. It functions as a compiled search engine that organizes vector spaces using hierarchical graphs, trees, and product quantization, while computing proximity through various mathematical distance formulas including Euclidean, Manhattan, cosine, and dot product calculations. The library supports high-performance execution through hardware-accelerated instruction sets and concurrent thread dispatch across multiple hardware processors. To accommo

USearch is a high-performance vector similarity search engine and approximate nearest neighbor index designed for dense embeddings. It functions as a low-level vector database core and high-dimensional vector indexer, providing the primitives necessary to store and retrieve vectors across massive datasets. The engine distinguishes itself through hardware-level SIMD acceleration for distance kernels and a proximity-graph indexing system that enables fast retrieval across billions of vectors. It supports multi-precision vector quantization to balance memory usage and accuracy, and utilizes memo

This project is a high-performance library designed for the similarity search and clustering of dense vectors across massive datasets. It functions as a vector similarity search engine, providing the necessary tools to organize complex numerical data into specialized structures that facilitate rapid retrieval and efficient querying of millions of records. The library distinguishes itself through a variety of advanced indexing and compression techniques, including hierarchical navigable small worlds for logarithmic time complexity and inverted file indexing to partition vector spaces into mana

zvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It functions as a hybrid search engine and a retrieval-augmented generation knowledge base, allowing for the storage and retrieval of dense and sparse vectors. The system is distinguished by its hybrid retrieval pipeline, which fuses vector similarity, full-text keyword matching, and scalar metadata filtering into single query operations. It supports a plugin-based model integration system for registering custom embedding models and rerankers, as well as language bindings for nativ

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

Hikyuu is a quantitative trading framework designed for developing, backtesting, and executing systematic trading strategies. It functions as a high-speed system that combines a financial time-series library, a multi-factor analysis tool, and a quantitative backtesting engine to support comprehensive trading research. The framework is distinguished by its high-speed computing core, which utilizes multi-threaded execution to process large volumes of market data for technical indicator generation. It supports a modular strategy composition model where signal, risk, and fund management component

This project is a C++ vector similarity engine and implementation of the Hierarchical Navigable Small World algorithm. It provides a header-only library for performing approximate nearest neighbor searches in high-dimensional spaces, alongside Python bindings that expose these indexing and search capabilities to data science environments. The engine enables real-time embedding retrieval and high-dimensional similarity search using a multi-layered graph structure to balance search speed and accuracy. It supports custom distance metrics to calculate similarity between vectors in various mathema

FalkorDB is a high-performance graph database management system and vector graph database. It serves as a knowledge graph construction tool and a GraphRAG knowledge store, integrating structured property graphs with vector search to provide grounded context for large language models. The engine is designed as a multi-tenant graph engine, capable of hosting thousands of isolated datasets within a single instance. The system distinguishes itself by using linear algebra for query execution, treating relationship tensors as matrix multiplications to achieve low-latency multi-hop traversals. It ut

Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

hnswlib is a header-only C++ library and vector indexing engine designed for high-dimensional approximate nearest neighbor search. It organizes large collections of embeddings into a searchable graph structure to enable rapid proximity queries and distance calculations. The system utilizes Hierarchical Navigable Small World graphs to achieve fast vector similarity search. It distinguishes itself by allowing the definition of custom distance metrics and similarity functions to adapt calculations to specific data requirements. The engine covers the full indexing lifecycle, including incrementa

SPTAG is a vector approximate nearest neighbor search library and distributed vector search engine. It provides a large-scale vector index designed to organize and retrieve similar vectors from massive datasets using high-performance similarity search and proximity queries. The system functions as a dynamic vector index manager, supporting incremental updates, insertions, and deletions of vectors without requiring a full index rebuild. It scales search operations across multiple machines to handle large-scale datasets and high volumes of online requests through distributed search request hand