Top 8 Python Libraries for Recommendation Systems

Here are the top Python libraries for recommendation systems that you can use for content-based recommendation systems, collaborative filtering recommendation systems, hybrid recommendation systems, demographic-based recommendation systems, utility-based recommendation systems, and knowledge-based recommendation systems.  

Recommender systems are a technique of recommending or finding ideas and products related to a user's way of thinking. It is one of the most efficient machine learning algorithms widely used in online shopping, on-demand movies, and music streaming.  

The top 8 Python libraries for recommendation systems are shown below, arranged by use case. Here is a detailed review of libraries.  

recommenders: 

• Contains best practices and examples to build recommendation systems, provided as Jupyter notebooks.  
• Helps in preparing and loading data for every recommender algorithm and evaluates algorithms with offline metrics. 
• Allows us to test our repository on various machines and offers us a quicker test cycle.  

annoy:  

• Use static files as indexes.  
• Uses tree-based algorithms.  
• Can use indexes created from static files.  
• Reduces the memory footprint to make indexes small.  

Surprise:  

• Library for building and evaluating recommendation systems.  
• Used for model-based collaborative filtering, neighborhood-based methods, and matrix factorization.  
• Provides tools to analyze, compare and evaluate the algorithms' performance.  

lightfm:  

• Used for building hybrid recommendation systems.  
• Combination of matrix factorization with content-based recommendations.  
• Recommendation system that effectively implements BPR and WARP ranking losses and implicit and explicit feedback.  

implicit:  

• Used for Collaborative Filtering for Implicit Datasets.  
• Can be used for matrix factorization.  
• Employed in applications, including institutions, education, learning, and artificial intelligence recommender systems.  

torchrec: 

• Is a PyTorch domain library built to provide common parallelism and sparsity primitives required for larger-scale recommendation systems.  
• Allows authors to train models with larger embedding tables shared across many GPUs.  
• Enables easy authoring of performant multi-device or multi-node models with the help of a hybrid model or data parallelism.  

tensorrec:  

• Used for deep learning-based recommendation systems.  
• Specifically designed for sparse, multi-relational data.  
• Uses deep learning algorithms like neural networks to model user-item interactions. 

TF-recomm: 

• Is a TensorFlow-based recommendation system.  
• With the TF-recomm, we can do derivative calculations using auto differentiation, i.e., you only require writing the inference part.  
• Offers various fancy SGD learning algorithms, distributed training in a computer cluster, and CPU/GPU acceleration.