An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.

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.

Must-read Papers for Recommender Systems (RS)

rectorch is a pytorch-based framework for state-of-the-art top-N recommendation

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

DataSets links for recommender systems research, in particular for transfer learning, user representation, pre-training,lifelong learning, cold start recommendation

Machine learning- based solution to the problem of duplicity in the bug reports repository.

This is the repository for the Master of Science thesis titled "GAN-based Matrix Factorization for Recommender Systems".

TensorFlow2 Implementation of "Neural Attentive Item Similarity Model for Recommendation"

The Implementation of "FISM: Factored Item Similarity Models for Top-N Recommender Systems"

A Worrying Reproducibility Study of Intent-Aware Recommendation Models

Customer segmentation of retail data using PCA + K-Means, Agglomerative, and DBSCAN/HDBSCAN, with cluster profiling via radar charts and histograms, and cluster-based top-3 product recommendations.

We share our code online": Why this is not enough to ensure reproducibility and progress in recommender systems research

A lightweight recommender that helps you discover your next learning resource. It blends patterns from similar users with content keywords, and explains each suggestion in the UI.

“We share our code online: Why this is not enough to ensure reproducibility and progress in recommender systems research

Implementation of z-scoREC and ImposeSVD for top-N recommendations