Evgeny Frolov

Source code to support ACM RecSys'16 paper "Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks"

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Accompanying code for the paper Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks, accepted at ACM RecSys 2020.

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Python progress bar with rich output that uses native ipython functionality. Widget-free. Works even in JupyterLab.

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Recommender system and evaluation framework for top-n recommendations tasks that respects polarity of feedbacks. Fast, flexible and easy to use. Written in python, boosted by scientific python stack.

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Accompanying code for reproducing experiments from the HybridSVD paper. Preprint is available at https://arxiv.org/abs/1802.06398.

evfro

Tensor-based recommender system that incorporates categorical contextual information into collaborative filtering workflow.

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