Five Best Open-Source ML Recommender System Projects

Recommender systems aim at foreseeing users’ interests and recommend interesting products. These systems have broad usage in the commercial sphere. They serve as one of the most powerful Machine Learning (ML) systems that help online merchants boost sales. Recommender systems have six types that generally work in the entertainment and media industry.

These include a content-based recommender system, demographic-based recommender system, and collaborative recommender system. Knowledge-based recommender systems, hybrid recommender systems, and utility recommender systems are also parts of recommendation systems.

Here are some open-source Machine Learning Recommender System Projects that can enable you to improve your skills in the DataScience field and Artificial Intelligence.

1. LightFM

LightFM is an easy-to-use and fast hybrid recommendation algorithm to produce high-quality results. It is a python implementation of LightFM for implicit and explicit feedback, including BPR and WARP ranking losses. The project incorporates product and user metadata into the conventional matrix factorization algorithms. LightFM represents every item and user as the summation of the dormant illustration of their features. It eventually enables recommendations to employ article features to simplify new things and use user features to generalize new users.

2. Spotlight

Spotlight intends to serve as a tool to explore and prototype new recommender models fast. It employs PyTorch to develop shallow and deep recommender models. The open-source project also incorporates utilities to create synthetic datasets.  Spotlight offers utilities for generating or fetching recommendation datasets, deep sequence model presentations, and shallow factorization. It gives numerous models and utilities to fit new item recommendation models, such as pooling models in YouTube recommendations.

3. Seldon Server

Seldon Server is a Machine Learning (ML) platform. Developed on Kubernetes Cluster, it is a Recommendation Engine, which gives an open-source data science stack. Seldon can use Machine Learning and Deep Learning models to produce on-premise or in the cloud. Some of the examples include Azure, AWS, and GCP. It comprises an API with two primary points. First is Predict, which helps build and use supervised ML models built in any ML framework at scale using microservices and containers. Recommend is another point that involves high-performance user activity and a recommendation engine with numerous algorithms to work out of the box.

4. Tensorrec

This ML open-source project refers to the Python framework and TensorFlow recommendation algorithm. The Python recommender system of TensorRec lets you develop recommendation algorithms and modify them with TensorFlow swiftly. TensorRec deals with data manipulation, scoring, and position to make recommendations. TensorRec allows you to customize the representation or embedding functions and loss functions. Every TensorRec system uses three data parts, such as item features, user features, and interactions, to learn to make and rank the recommendations for users’ interests.

5. Implicit

Every model contains a multi-threaded training routine with OpenMP and Cython to make it equally suitable for the models among all CPU cores. Implicit can also use approximate nearest neighbor libraries like Faiss, Annoy, and NMSLIB to accelerate recommendation making. Furthermore, both BPR and ALS models have custom CUDA kernels that make fitting compatible GPUs possible.