This repository serves as a comprehensive, community-driven list of recommender systems and related resources, aiming to provide a centralized reference for developers, researchers, and practitioners. It facilitates comparison of various recommendation engines, datasets, and benchmarking tools by curating information on both Software-as-a-Service (SaaS) and open-source solutions, helping users navigate the complex landscape of recommendation technologies and identify suitable tools for their specific needs.

How It Works

The project is structured as a curated list, categorizing recommender systems into SaaS, Open Source, Non-SaaS Product, and Academic. Each entry typically includes a brief description, its focus (e.g., e-commerce, media, academic), underlying technologies, and sometimes its development status or notable features. It also compiles lists of relevant datasets, benchmarking frameworks, and media recommendation applications, offering a broad overview of the recommender systems ecosystem and its components.

Quick Start & Requirements

This repository is a curated list of resources and does not contain software to install or run. Users are directed to individual project links for setup, requirements, and usage instructions.

Highlighted Details

• Features a wide array of SaaS recommender systems such as Peerius, Strands, Recombee, and Google Recommendations AI, alongside numerous open-source projects including Universal Recommender, PredictionIO, LightFM, Surprise, Microsoft Recommenders, Nvidia Merlin, and RecBole.
• Includes academic systems like LensKit and LibRec, alongside benchmarking tools such as RiVaL and Idomaar, providing a spectrum of research and evaluation resources.
• Compiles a detailed table of common datasets used in recommender systems research, including Amazon Review, MovieLens, MIND, and PixelRec, with metadata and direct links for easy access.
• Lists specific media recommendation applications like Jinni, TasteKid, and Pandora, alongside foundational books and best practices for building recommendation systems.
• Highlights systems leveraging diverse technologies from Apache Spark and Hadoop to TensorFlow and CUDA, catering to various scales and complexities, from collaborative filtering to deep learning models.

Maintenance & Community

The repository is maintained by grahamjenson (@grahamjenson), who actively encourages community contributions through pull requests and direct contact via Twitter for corrections and additions, fostering an evolving resource.

Licensing & Compatibility

No specific software license is mentioned for the repository itself or for the listed projects within the provided README content. Users are advised to verify licensing for individual recommender systems or datasets before adoption.

Limitations & Caveats

Several listed open-source projects are noted as abandoned or neglected, indicating potential issues with ongoing support or development for those specific entries. The repository's value is dependent on the accuracy and currency of community contributions, and it serves as a directory rather than a functional tool.