How to Get Started With Recommender Systems-3

[swapneel-panda-419bc751 (Swapneel Panda)]

Recommender Systems Libraries

You probably don’t need to dive into the start of the art, at least not immediately.

As such, standard machine learning libraries are a great place to start.

For example, you can develop an effective recommender system using matrix factorization methods (SVD) or even a straight forward k-nearest neighbors model by items or by users.

As such, I recommend starting with some experiments with scikit-learn:

• Scikit-Learn Python Machine Learning Library.

You can practice on standard recommender system datasets if your own data is not yet accessible or available, or you just want to get the hang of things first.

Popular standard datasets for recommender systems include:

• MovieLens
• Yahoo datasets (music, urls, movies, etc.)

If you are ready for state-of-the-art techniques, a great place to start is “papers with code” that lists both academic papers and links to the source code for the methods described in the paper:

• Papers With Code: Recommendation Systems

There are a number of proprietary and open-source libraries and services for recommender systems.

I recommend sticking with open-source Python libraries in the beginning, such as:

• Surprise: A Python scikit for building and analyzing recommender systems
• Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems