# recommenders-team/recommenders

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21,769 stars · 3,325 forks · Python · MIT

## Links

- GitHub: https://github.com/recommenders-team/recommenders
- Homepage: https://recommenders-team.github.io/recommenders/intro.html
- awesome-repositories: https://awesome-repositories.com/repository/recommenders-team-recommenders.md

## Topics

`ai` `artificial-intelligence` `data-science` `deep-learning` `jupyter-notebook` `kubernetes` `machine-learning` `operationalization` `python` `ranking` `rating` `recommendation` `recommendation-algorithm` `recommendation-engine` `recommendation-system` `recommender` `tutorial`

## Description

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 evaluation toolkit for measuring recommendation quality through diversity analysis, novelty, and ranking metrics.

The system covers the full development lifecycle, including data engineering for interaction datasets, hyperparameter tuning, and distributed model training across CPU and GPU clusters. It further provides tools for performance benchmarking, API load testing, and model effectiveness tracking via A/B testing and conversion rates.

The project includes command-line utilities for parameterized notebook execution to validate system behavior.

## Tags

### Artificial Intelligence & ML

- [Recommender Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/recommender-systems.md) — A comprehensive framework for building and training personalized item suggestion engines using various filtering strategies.
- [Collaborative Filtering Models](https://awesome-repositories.com/f/artificial-intelligence-ml/collaborative-filtering-models.md) — Implements collaborative filtering architectures to predict user preferences based on shared interaction patterns.
- [Collaborative Filtering Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/collaborative-filtering-utilities.md) — Implements collaborative filtering to predict user preferences based on shared historical interaction patterns. ([source](https://github.com/recommenders-team/recommenders/blob/main/GLOSSARY.md))
- [Content-Based Filtering](https://awesome-repositories.com/f/artificial-intelligence-ml/content-based-filtering.md) — Implements content-based filtering by analyzing similarities between user profiles and item metadata. ([source](https://github.com/recommenders-team/recommenders/blob/main/GLOSSARY.md))
- [Recommendation Inference Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/end-to-end-inference-pipelines/recommendation-inference-pipelines.md) — Constructs end-to-end recommendation inference pipelines that prepare data and serve results in real-time. ([source](https://github.com/recommenders-team/recommenders/tree/main/examples))
- [Knowledge Graph Construction](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/knowledge-graph-engineering/knowledge-graph-construction.md) — Integrates automated processes for building knowledge graph structures to provide richer context for recommendations.
- [Interaction Matrix Factorizers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/model-hubs-and-pre-made-models/model-weights/latent-factor-analyzers/interaction-matrix-factorizers.md) — Implements matrix factorization algorithms to decompose user-item interaction data into latent factor representations.
- [Model Evaluation and Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-analysis.md) — Provides a toolkit for measuring recommendation quality through ranking metrics, accuracy benchmarks, and diversity analysis.
- [Model Evaluation Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-and-validation/model-evaluation-metrics.md) — Provides a specialized toolkit for measuring the accuracy and quality of recommendations using ranking and precision metrics.
- [Recommendation Data Engineering](https://awesome-repositories.com/f/artificial-intelligence-ml/recommendation-data-engineering.md) — Provides a comprehensive workflow for preparing, cleaning, and splitting interaction datasets for ML models.
- [Recommendation Models](https://awesome-repositories.com/f/artificial-intelligence-ml/recommendation-models.md) — Implements classical and deep learning algorithms, such as matrix factorization and gradient boosting, for personalized suggestions. ([source](https://recommenders-team.github.io/recommenders/intro.html))
- [Top-K Ranking Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/top-k-ranking-pipelines.md) — Implements a top-k ranking pipeline to filter and generate the final list of recommended items.
- [Ranking Relevance Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/context-aware-retrieval/context-relevance-evaluators/ranking-relevance-metrics.md) — Determines ranking quality using precision, recall, NDCG, and Mean Average Precision. ([source](https://github.com/recommenders-team/recommenders/blob/main/examples/03_evaluate))
- [Dataset Splitting Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-management/evaluation-datasets/dataset-splitting-utilities.md) — Offers tools for dividing data into training and testing sets using random, chronological, or stratified methods. ([source](https://github.com/recommenders-team/recommenders/blob/main/examples/01_prepare_data))
- [Stratified Splitting Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-management/evaluation-datasets/stratified-splitting-tools.md) — Provides stratified dataset splitting to ensure representative model evaluation.
- [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training.md) — Distributes the training of large-scale recommendation models across CPU clusters and GPU devices. ([source](https://github.com/recommenders-team/recommenders/tree/main/examples))
- [Large Scale Training](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-training.md) — Provides capabilities for scaling model training and data processing across distributed CPU and GPU clusters.
- [Hyperparameter Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/training-algorithms/machine-learning-optimization/hyperparameter-tuning.md) — Includes iterative processes for optimizing model hyperparameters to improve the precision of recommendations.
- [Rating Accuracy Evaluators](https://awesome-repositories.com/f/artificial-intelligence-ml/recognition-accuracy-evaluation/rating-accuracy-evaluators.md) — Calculates the error between predicted and actual ratings using Root Mean Square Error and Mean Absolute Error. ([source](https://github.com/recommenders-team/recommenders/blob/main/examples/03_evaluate))
- [Ranking Performance Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/recognition-accuracy-evaluation/top-k-accuracy-evaluators/ranking-performance-metrics.md) — Includes tools for quantifying recommendation quality using ranking performance metrics like NDCG and RMSE.
- [Recommendation Quality Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/recommendation-models/recommendation-quality-metrics.md) — Quantifies recommendation properties such as novelty, diversity, serendipity, and coverage. ([source](https://github.com/recommenders-team/recommenders/blob/main/examples/03_evaluate))
- [Recommendation List Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/recommender-systems/recommendation-list-generators.md) — Generates Top-K recommendation lists by calculating item similarities and filtering for the highest scoring candidates. ([source](https://github.com/recommenders-team/recommenders/blob/main/contrib/sarplus/README.md))

### Data & Databases

- [Knowledge Graph Construction Tools](https://awesome-repositories.com/f/data-databases/knowledge-graph-construction-tools.md) — Includes frameworks for constructing structured knowledge graphs from external data to enrich recommendation context. ([source](https://github.com/recommenders-team/recommenders/blob/main/examples/01_prepare_data))
- [Knowledge Graph Recommendations](https://awesome-repositories.com/f/data-databases/knowledge-graph-indexers/knowledge-graph-builders/knowledge-graph-recommendations.md) — Generates recommendations by leveraging structured knowledge graph data to explore entity relationships. ([source](https://github.com/recommenders-team/recommenders/blob/main/GLOSSARY.md))

### Development Tools & Productivity

- [Machine Learning Pipelines](https://awesome-repositories.com/f/development-tools-productivity/task-pipeline-managers/machine-learning-pipelines.md) — Ships an end-to-end machine learning pipeline for orchestrating data preparation, hyperparameter tuning, and model deployment.

### Part of an Awesome List

- [Evaluation and Benchmarking](https://awesome-repositories.com/f/awesome-lists/ai/evaluation-and-benchmarking.md) — Provides standardized datasets and frameworks for measuring the accuracy and effectiveness of recommendation algorithms. ([source](https://github.com/recommenders-team/recommenders/tree/main/examples))
- [Dataset Loading Utilities](https://awesome-repositories.com/f/awesome-lists/data/recommendation-datasets/dataset-loading-utilities.md) — Ships tools to import industry-standard datasets into dataframes for algorithm testing and benchmarking. ([source](https://github.com/recommenders-team/recommenders/tree/main/recommenders))
- [Recommender Systems](https://awesome-repositories.com/f/awesome-lists/ai/recommender-systems.md) — Best practices and benchmarks for building recommendation systems.

### Business & Productivity Software

- [A/B Testing](https://awesome-repositories.com/f/business-productivity-software/a-b-testing.md) — Tracks model effectiveness in production using A/B testing and conversion rate metrics. ([source](https://github.com/recommenders-team/recommenders/blob/main/AUTHORS.md))
- [Business Recommendation Patterns](https://awesome-repositories.com/f/business-productivity-software/recommendation-engines/business-recommendation-patterns.md) — Provides pre-defined frameworks and patterns for common business use cases to accelerate system deployment. ([source](https://github.com/recommenders-team/recommenders/blob/main/scenarios))

### DevOps & Infrastructure

- [Cloud Container Deployments](https://awesome-repositories.com/f/devops-infrastructure/cloud-container-deployments.md) — Supports the deployment of recommendation models to production using container orchestration and cloud databases. ([source](https://github.com/recommenders-team/recommenders/blob/main/examples/05_operationalize))
- [Compute Throughput Optimizers](https://awesome-repositories.com/f/devops-infrastructure/performance-optimization-utilities/compute-throughput-optimizers.md) — Scales computations across distributed nodes using memory mapping, indexing, and data partitioning. ([source](https://github.com/recommenders-team/recommenders/blob/main/contrib/sarplus/README.md))

### Education & Learning Resources

- [Data Formatting Utilities](https://awesome-repositories.com/f/education-learning-resources/educational-resources/algorithms-theory-academics/cs-theory-foundations/algorithms/general-collections-and-study/algorithm-implementations/recommendation-algorithms/data-formatting-utilities.md) — Provides utilities to format datasets into specific structures required by recommendation algorithms. ([source](https://recommenders-team.github.io/recommenders/intro.html))