python-recsys Reviews & Product Details

I like the capacity of the tool to work with a large quantity of data Review collected by and hosted on G2.com.

I'm not too fond of the interface. It is not very attractive to work with Review collected by and hosted on G2.com.

It's an open-source rep that allow the user to install easily. the coding experience is very helpful and easy Review collected by and hosted on G2.com.

Nothing to say about dislike. The rep is very helpful for anyone Review collected by and hosted on G2.com.

Python-recsys is a powerful python library that consist in a implementation of a recommeder system.

You can recommend an item providing a user-based o item-based mechanism.

Some matrix decomposition algorithms are implemented, such as singular value decomposition, and you can evaluate results through standard performance measures, in order to find best tuning params for your specific domain.

Algorithms are supported with a great documentation and a lot of datasets to experiment. Review collected by and hosted on G2.com.

Python-recsys doesn't work with python 3, so if you have legacy projects in python 2, you cannot integrate it.

Some well known algorithms are not implemented and is not possibile to add variants to implemented ones

The lib users community is not very large, so don't expect to have a lot of interactions if you have some problems in using the library. Review collected by and hosted on G2.com.

Python-recsys is open source platform and current trend is open source technology. and its provide to best Library in different different functionality based. Review collected by and hosted on G2.com.

Python Bug Tracker services are not god in my side. its my own experience. sorry for not all side dislike in Bug Tracker . but my side some issues .. Review collected by and hosted on G2.com.

The Python-recsys library is great once you get past the bland documentation. It makes great usage of Numpy and Scipy to offer an incredibly accurate recommender system. Review collected by and hosted on G2.com.

It is 2019 and there is still no support for python 3, unfortunately. This was ultimately the reason why we did not implement it into our environment after testing. Review collected by and hosted on G2.com.

In the models ive used, the hybrid model is great for having accurate predictive output and recommendations. Review collected by and hosted on G2.com.

There can be some issues that arise when dealing with very big data. Review collected by and hosted on G2.com.

We used Python for our automation deployment tool & its so nice fast in processing while comparing to other things. Compatibility wise, scalability & performance. Coding, execution-wise it's so simple & easy to do. Review collected by and hosted on G2.com.

Nothing to say anything wrong about this. its quite easy to use any kind of tools & compatible with all. Review collected by and hosted on G2.com.

The library does really well what its supposed to do with recommendation system, specifically in our implementation of retail sites Review collected by and hosted on G2.com.

There are workarounds to some things but there are some core features missing like missing frequently viewed recommendations, etc. Review collected by and hosted on G2.com.

Its a great technical software. Not easy to use without experience. Review collected by and hosted on G2.com.

Highly technical, great for the people with experience. Review collected by and hosted on G2.com.

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.

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