Displaying 1 to 6 from 6 results

librec - LibRec: A Leading Java Library for Recommender Systems, see

  •    Java

LibRec (http://www.librec.net) is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms, aiming to resolve two classic recommendation tasks: rating prediction and item ranking. A movie recommender system is designed and available here.

ranking - Learning to Rank in TensorFlow

  •    Python

We envision that this library will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications. TF-Ranking was presented at premier conferences in Information Retrieval, SIGIR 2019 and ICTIR 2019! The slides are available here.

goodbooks-10k - Ten thousand books, six million ratings

  •    Jupyter

Some of these files are quite large, so GitHub won't show their contents online. See samples/ for smaller CSV snippets. Open the notebook for a quick look at the data. Download individual zipped files from releases.

vae_cf - Variational autoencoders for collaborative filtering

  •    Jupyter

This notebook accompanies the paper "Variational autoencoders for collaborative filtering" by Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara, in The Web Conference (aka WWW) 2018. In this notebook, we show a complete self-contained example of training a variational autoencoder (as well as a denoising autoencoder) with multinomial likelihood (described in the paper) on the public Movielens-20M dataset, including both data preprocessing and model training.




sbr-rs - Deep recommender systems for Rust

  •    Rust

An implementation of sequence recommenders based on the wyrm autdifferentiaton library. sbr implements efficient recommender algorithms which operate on sequences of items: given previous items a user has interacted with, the model will recommend the items the user is likely to interact with in the future.






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