If you are comfortable with Python, using this for recommendation engines will be easy. Accommodates a variety of algorithm types including classification recommendations, popularity based and recall. Review collected by and hosted on G2.com.

What do you dislike about python-recsys?

Tedious to install updated systems. Some libraries don't work on certain systems Review collected by and hosted on G2.com.

GA

Verified User in Accounting

Mid-Market (51-1000 emp.)

11/1/2018

"Has everything for your programming needs"

4.5/5

What do you like best about python-recsys?

Includes a big library in order for you to be able to implement what you need in your algorithm, makes good use for what you need to complete your task at hand. Techniques are very developed. Review collected by and hosted on G2.com.

What do you dislike about python-recsys?

This software is not available for one of the newer python. Review collected by and hosted on G2.com.

Recommendations to others considering python-recsys:

If you know how to use python, then use this to further advance your algorithm. Get the opportunity to analyze your data even better for better work flow. Review collected by and hosted on G2.com.

What problems is python-recsys solving and how is that benefiting you?

Being able to organize data and offer solutions. Review collected by and hosted on G2.com.

AA

Anastasia A.

Analista de sistema

Small-Business (50 or fewer emp.)

3/28/2018

"python-recsys Library for Recommender Systems"

4.5/5

What do you like best about python-recsys?

the python-recsys Library (https://github.com/ocelma/python-recsys) offer us the opportunity to evaluate libraries in the field of machine learning for python, to test the technological bases for building recommendation systems. The solution makes use of the python libraries: python-scipy, python-numpy, csc-pysparse, networkx, divisi2. The solution offers recommendations and predictions to the users of a system, through the transformation of input data, based on reactions and transactions of the users and their relationship with the components of the products with which they interact. It makes use of the SVD (singular-value decomposition) functionality to apply a factorization process of the user's valuation data entry matrix. This solution can be used for the construction of systems that need to predict product recommendations, to its users, when there is a high number of products and users, efficiently taking advantage of the transactions and interactions of users and products. It is a good tool to learn about machine learning systems, making use of statistical algorithms and innovative development techniques, very well constructed, with some of the best programming languages that exist: python. Very good library. highly recommended its use and implementation Review collected by and hosted on G2.com.

What do you dislike about python-recsys?

the libraries are not available for python 3.* Review collected by and hosted on G2.com.

Recommendations to others considering python-recsys:

is a very good library for the construction of complex system of recommendations through factoring processes of input data valuation of a collaborative system Review collected by and hosted on G2.com.

What problems is python-recsys solving and how is that benefiting you?

libraries could help me build a system to make recommendations to users about recipes according to the most popular products among users Review collected by and hosted on G2.com.