evfro/recsys19_hybridsvd

This repository has been archived on 01/Sep/2022
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evfro/recsys19_hybridsvd

evfro

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

polara

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.

ipypb

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

HyperbolicRecommenders

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

fifty-shades

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

Jupyter Notebook

TensorGlue

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

Stable_Recommendations

The accompanying code for our paper.

Jupyter Notebook

acaml2018_recsys

Jupyter Notebook

RecSys_ISP2017

ISP Course at Skoltech

Jupyter Notebook

hybridsvd

Supplementary materials to reproduce experiments

Jupyter Notebook

cofida

Fast in-memory pytorch dataloaders designed specifically for collaborative filtering data.

RecSysLeaderboardApp