Top 10 Open Source Alternatives to eRecommender Product Suggestions in 2025

As the demand for efficient recommendation systems continues to grow, 2025 has introduced several powerful open-source alternatives to eRecommender. These tools offer unique features and capabilities for various recommendation needs. This article explores the top 10 open-source recommendation systems, comparing them on key data points.

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1. Gorse

Description: Gorse is an open-source recommendation system written in Go, designed for ease of integration and automatic model training.
Data Points:
Multi-Source Recommendations: Supports recommendations from popular items, latest items, user-based, item-based, and collaborative filtering.
Automated Machine Learning (AutoML): Automatically searches for the best recommendation model in the background.
Distributed Prediction: Allows horizontal scaling during the recommendation stage.
RESTful APIs: Exposes APIs for data management and recommendation requests.
Multi-Database Support: Compatible with Redis, MySQL, Postgres, MongoDB, and ClickHouse. Learn more about Gorse

2. LightFM

Description: LightFM is a Python implementation of hybrid recommendation algorithms combining collaborative and content-based filtering, known for its speed and scalability.
Data Points:
Hybrid Approach: Combines collaborative and content-based filtering for better recommendations.
Scalability: Uses Cython, enabling it to handle very large datasets on multi-core machines.
Active Development: Actively developed and used in production at various companies.
Implicit and Explicit Feedback: Supports both types of feedback data.
Learning-to-Rank Algorithms: Uses learning to rank methods to make recommendations. Explore LightFM

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3. Surprise

Description: Surprise is a Python library focused on explicit rating data, designed for building and analyzing recommender systems.
Data Points:
Variety of Algorithms: Offers algorithms like collaborative filtering and matrix factorization techniques.
Built-in Datasets: Includes datasets for testing and benchmarking purposes.
Cross-Validation: Supports cross-validation and hyperparameter tuning for model optimization.
Custom Algorithms: Allows users to implement custom algorithms by extending base classes.
User-friendly: Designed for easy experimentation with different algorithms. Learn more about Surprise

4. TensorFlow Recommenders (TFRS)

Description: TFRS is a TensorFlow library that simplifies the creation of recommendation systems using deep learning techniques.
Data Points:
Deep Learning Focus: Built on TensorFlow, utilizing deep learning models.
Flexible Framework: Provides a flexible framework for creating complex recommendation models.
Scalability: Designed to handle large datasets and complex models.
Customizable: Allows for customization of model architecture and training pipelines.
Integration with TensorFlow: Seamlessly integrates with the TensorFlow ecosystem. Discover TensorFlow Recommenders

5. Implicit

Description: Implicit is a Python library offering fast collaborative filtering implementations for implicit feedback datasets.
Data Points:
Focus on Implicit Data: Optimized for handling implicit feedback datasets (e.g., clicks, views, purchases).
Speed and Efficiency: Provides fast implementations of popular recommendation algorithms.
Multi-threaded Training: Uses Cython and OpenMP for parallel model fitting across available CPU cores.
GPU Support: Can utilize GPUs to enhance the performance of model training.
Active Development: Actively maintained and frequently updated. Explore Implicit

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6. LensKit

Description: LensKit is a toolkit for building, researching, and learning about recommender systems, focusing on research and educational purposes.
Data Points:
Flexible and Research-oriented: Designed for training, running, and evaluating recommender algorithms in a research setting.
PyData Ecosystem Integration: Compatible with Scikit-learn, TensorFlow, and PyTorch for experiments.
Successor to Java LensKit: Python version (LKPY) enables robust and reproducible experiments using the PyData ecosystem.
Support for Variety of Algorithms: Provides support for many different recommender algorithms.
Open Source Toolkit: Facilitates the process of building, researching, and learning recommender systems. Discover LensKit

7. Rexy

Description: Rexy is an open-source recommendation system based on a User-Product-Tag concept, designed for flexibility with various data schemas.
Data Points:
Flexible Structure: Designed to adapt to various data schemas, making it versatile.
Pythonic: Written in a highly optimized Pythonic way, making it flexible against changes.
NoSQL Database: Uses Aerospike as a database engine for high speed and scalability.
User-Product-Tag Concept: Uses a general concept for user, product, and tags for more versatile recommendation.
Highly Optimized: Built to be highly optimized, comprehensive, and Pythonic. Learn more about Rexy

8. Mahout

Description: Apache Mahout is an open-source library focused on scalable machine learning algorithms, including those for recommendation systems.
Data Points:
Scalability: Designed for large datasets and integration with Apache Hadoop.
Collaborative and Content-Based Filtering: Offers various algorithms for both collaborative and content-based filtering.
Big Data Compatible: Integrates with Hadoop for large-scale data processing.
Versatile: Suitable for a wide range of recommendation needs.
Open Source: It is an open-source library that is free to use. Explore Mahout

9. Spotlight

Description: Spotlight is a Python recommender system using PyTorch for building both deep and shallow recommender models.
Data Points:
PyTorch Backend: Uses the PyTorch framework for model building.
Deep and Shallow Models: Supports both deep and shallow recommendation models.
Well-implemented Python Framework: Facilitates the creation of basic recommendation systems.
Factorization and Sequence Models: Employs factorization and sequence models.
Python Implementation: Written in Python for accessibility and ease of use. Learn more about Spotlight

10. TensorRec

Description: TensorRec is a Python recommendation system designed for quick development and customization of recommendation algorithms using TensorFlow.
Data Points:
TensorFlow-based: Uses TensorFlow for building and training recommendation models.
Customizable: Allows users to develop and customize recommendation algorithms.
Python Implementation: Written in Python, making it accessible for a wider audience.
Fast Development: Designed for quick development of customized algorithms.
Versatile: Supports building recommendation models and customizing them according to your needs. Discover TensorRec

This comprehensive list of open-source recommendation systems in 2025 provides a variety of options to explore based on your specific needs and capabilities.

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FAQ

1. What is a good open-source alternative to eRecommender for product suggestions?

Gorse is a robust alternative with features like multi-source recommendations, automated machine learning, and support for multiple databases. Learn more about Gorse

2. Which recommendation system excels in scalability and speed using Python?

LightFM combines collaborative and content-based filtering with high scalability and speed, making it a great choice for large datasets. Explore LightFM

3. What is an easy-to-use Python library for building recommenders?

Surprise is user-friendly and ideal for experimenting with different algorithms and explicit rating data. Discover Surprise

4. Is there an open-source recommendation system leveraging deep learning?

TensorFlow Recommenders (TFRS) uses TensorFlow to create complex and scalable recommendation models. Learn more about TFRS

5. What recommendation system is optimized for implicit feedback datasets?

Implicit provides fast and efficient implementations for handling implicit feedback like clicks and views. Check out Implicit

6. Which toolkit is best for researching and learning about recommendation systems?

LensKit is designed for flexibility and research, integrating well with the PyData ecosystem. Explore LensKit

7. What is a flexible recommendation system for various data schemas?

Rexy is versatile and optimized, using a User-Product-Tag concept with a NoSQL database for high speed. Discover Rexy

8. Which open-source library is suitable for big data and integrates with Hadoop?

Mahout is tailored for scalable machine learning and recommendation algorithms, ideal for big data contexts. Learn more about Mahout

9. What Python system uses PyTorch for deep and shallow models in product recommendations?

Spotlight builds both deep and shallow recommendation models, leveraging PyTorch for a robust backend. Explore Spotlight

10. Which system allows for quick development and customization using TensorFlow?

TensorRec provides a customizable and fast development environment for creating recommendation algorithms. Discover TensorRec

References

About the Author

Violetta Bonenkamp, also known as MeanCEO, is an experienced startup founder with an impressive educational background including an MBA and four other higher education degrees. She has over 20 years of work experience across multiple countries, including 5 years as a solopreneur and serial entrepreneur. She’s been living, studying and working in many countries around the globe and her extensive multicultural experience has influenced her immensely.

Violetta is a true multiple specialist who has built expertise in Linguistics, Education, Business Management, Blockchain, Entrepreneurship, Intellectual Property, Game Design, AI, SEO, Digital Marketing, cyber security and zero code automations. Her extensive educational journey includes a Master of Arts in Linguistics and Education, an Advanced Master in Linguistics from Belgium (2006-2007), an MBA from Blekinge Institute of Technology in Sweden (2006-2008), and an Erasmus Mundus joint program European Master of Higher Education from universities in Norway, Finland, and Portugal (2009).

She is the founder of Fe/male Switch, a startup game that encourages women to enter STEM fields, and also leads CADChain, and multiple other projects like the Directory of 1,000 Startup Cities with a proprietary MeanCEO Index that ranks cities for female entrepreneurs. Violetta created the "gamepreneurship" methodology, which forms the scientific basis of her startup game. She also builds a lot of SEO tools for startups. Her achievements include being named one of the top 100 women in Europe by EU Startups in 2022 and being nominated for Impact Person of the year at the Dutch Blockchain Week. She is an author with Sifted and a speaker at different Universities. Recently she published a book on Startup Idea Validation the right way: from zero to first customers and beyond and launched a Directory of 1,500+ websites for startups to list themselves in order to gain traction and build backlinks.

For the past several years Violetta has been living between the Netherlands and Malta, while also regularly traveling to different destinations around the globe, usually due to her entrepreneurial activities. This has led her to start writing about different locations and amenities from the POV of an entrepreneur. Here’s her recent article about the best hotels in Italy to work from.

About the Publication

Fe/male Switch is an innovative startup platform designed to empower women entrepreneurs through an immersive, game-like experience. Founded in 2020 during the pandemic "without any funding and without any code," this non-profit initiative has evolved into a comprehensive educational tool for aspiring female entrepreneurs.The platform was co-founded by Violetta Shishkina-Bonenkamp, who serves as CEO and one of the lead authors of the Startup News branch.

Mission and Purpose

Fe/male Switch Foundation was created to address the gender gap in the tech and entrepreneurship space. The platform aims to skill-up future female tech leaders and empower them to create resilient and innovative tech startups through what they call "gamepreneurship". By putting players in a virtual startup village where they must survive and thrive, the startup game allows women to test their entrepreneurial abilities without financial risk.

Key Features

The platform offers a unique blend of news, resources,learning, networking, and practical application within a supportive, female-focused environment:

Skill Lab: Micro-modules covering essential startup skills
Virtual Startup Building: Create or join startups and tackle real-world challenges
AI Co-founder (PlayPal): Guides users through the startup process
SANDBOX: A testing environment for idea validation before launch
Wellness Integration: Virtual activities to balance work and self-care
Marketplace: Buy or sell expert sessions and tutorials

Impact and Growth

Since its inception, Fe/male Switch has shown impressive growth:

3,000+ female entrepreneurs in the community
100+ startup tools built
5,000+ pieces of articles and news written

Partnerships

Fe/male Switch has formed strategic partnerships to enhance its offerings. In January 2022, it teamed up with global website builder Tilda to provide free access to website building tools and mentorship services for Fe/male Switch participants.

Recognition

Fe/male Switch has received media attention for its innovative approach to closing the gender gap in tech entrepreneurship. The platform has been featured in various publications highlighting its unique "play to learn and earn" model.