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lightfm - A Python implementation of LightFM, a hybrid recommendation algorithm.

  •    Python

LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results. It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features).

cofactor - CoFactor: Regularizing Matrix Factorization with Item Co-occurrence

  •    Jupyter

This repository contains the source code to reproduce the experimental results as described in the paper "Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence" (RecSys'16). Note: The code is mostly written for Python 2.7. For Python 3.x, it is still usable with minor modification. If you run into any problem with Python 3.x, feel free to contact me and I will try to get back to you with a helpful solution.

binge - Recommendation models that use binary rather than floating point operations at prediction time

  •    TeX

This repository contains an implementation of recommendation models that use binary rather than floating point operations at prediction time. This makes them much faster (and less memory intensive), but also less accurate. The details are in the paper and in the slides.