--- title: python-recsys Reviews meta_title: 'python-recsys Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 14 reviews by the users' company size, role or industry to find out how python-recsys works for a business like yours. aggregate_rating: rating_value: 4.5 review_count: 14 scale: '5' date_modified: '2026-07-12' parent_category: name: Artificial Intelligence url: https://www.g2.com/categories/artificial-intelligence --- # python-recsys Reviews **Vendor:** BLLIP Parser **Category:** [Machine Learning Software](https://www.g2.com/categories/machine-learning) **Average Rating:** 4.5/5.0 **Total Reviews:** 14 ## About python-recsys python-recsys is a python library for implementing a recommender system. ## python-recsys Reviews ### 1. A good toolkit for implementing a complex recommender system **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** January 31, 2019 **What do you like best about python-recsys?** 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. **What do you dislike about python-recsys?** 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. **Recommendations to others considering python-recsys:** Keep attention to python version. For using this lib you have to study some technicalities in recommender systems and intelligent systems. **What problems is python-recsys solving and how is that benefiting you?** I've implemented a sophisticated recommender system to recommend e-commerce item in a cotext-aware environment. It was very simply, with some knowledge of theory paradigm in recommeder systems and personalized intelligent systems. ### 2. Recommended **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Program Development | Small-Business (50 or fewer emp.) **Reviewed Date:** March 19, 2019 **What do you like best about python-recsys?** Makes it easier with handling of datasets and is quite easy to implement. There is a learning curve but you can get over that quite easily. **What do you dislike about python-recsys?** It is a new origins library and the searching algorithim doesn't always work for what you'll need. It still seems a bit new and undeveloped but time will tell. **Recommendations to others considering python-recsys:** It is good for an e commerce item and we can see a good use of it in the future. **What problems is python-recsys solving and how is that benefiting you?** It is useful for analyzing CC data. I think that you can implement and use it for a host of different tasks as well. ### 3. Fairly easy to pick up **Rating:** 4.0/5.0 stars **Reviewed by:** Jay B. | Engineering Intern, Computer Software, Enterprise (> 1000 emp.) **Reviewed Date:** January 21, 2019 **What do you like best about python-recsys?** 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. **What do you dislike about python-recsys?** 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. **Recommendations to others considering python-recsys:** This is your go-to if you do not require Python 3 support. **What problems is python-recsys solving and how is that benefiting you?** This library can be used to create a recommender system that makes sound predictions based on user behavior. ### 4. A great decision recommendation Library **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Facilities Services | Mid-Market (51-1000 emp.) **Reviewed Date:** March 19, 2019 **What do you like best about python-recsys?** In the models ive used, the hybrid model is great for having accurate predictive output and recommendations. **What do you dislike about python-recsys?** There can be some issues that arise when dealing with very big data. **What problems is python-recsys solving and how is that benefiting you?** Recsys uses a SVD model which is beneficial for sparse matrix, you can use hybrid filtering and there is a lot of support for this library. ## python-recsys Discussions - [What is python-recsys used for?](https://www.g2.com/discussions/what-is-python-recsys-used-for) - [View python-recsys pricing details and edition comparison](https://www.g2.com/products/python-recsys/reviews?filters%5Bnps_score%5D%5B%5D=4§ion=pricing&secure%5Bexpires_at%5D=2026-08-01+23%3A57%3A58+-0500&secure%5Bsession_id%5D=919502ae-9b46-483b-9fe3-c21ad8fb88ab&secure%5Btoken%5D=3aed8a7973a35e5e78350340935667a87052b7773faf6227e015362157c813f3&format=llm_user) ## python-recsys Features **Integration - Machine Learning** - Integration **Learning - Machine Learning** - Training Data - Actionable Insights - Algorithm ## Top python-recsys Alternatives - [Alteryx](https://www.g2.com/products/alteryx/reviews) - 4.6/5.0 (856 reviews) - [Phrase](https://www.g2.com/products/phrase-phrase/reviews) - 4.5/5.0 (1,291 reviews) - [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) - 4.3/5.0 (654 reviews)