Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Cofactor | 155 | 6 years ago | apache-2.0 | Jupyter Notebook | ||||||
CoFactor: Regularizing Matrix Factorization with Item Co-occurrence | ||||||||||
Kitabe | 127 | 6 months ago | 2 | mit | HTML | |||||
Book Recommendation System built for Book Lovers📖. Simply Rate ⭐ some books and get immediate recommendations🤩 | ||||||||||
Nlp_adversarial_examples | 112 | 4 years ago | mit | Jupyter Notebook | ||||||
Implementation code for the paper "Generating Natural Language Adversarial Examples" | ||||||||||
Bane | 86 | 2 months ago | 1 | gpl-3.0 | Python | |||||
A sparsity aware implementation of "Binarized Attributed Network Embedding" (ICDM 2018). | ||||||||||
Asne | 76 | 7 months ago | gpl-3.0 | Python | ||||||
A sparsity aware and memory efficient implementation of "Attributed Social Network Embedding" (TKDE 2018). | ||||||||||
Lorentz Embeddings | 62 | a year ago | 2 | mit | Python | |||||
Embed arbitrary graphs in Hyperbolic space | ||||||||||
Regal | 54 | 2 years ago | 7 | mit | Python | |||||
Representation learning-based graph alignment based on implicit matrix factorization and structural embeddings | ||||||||||
Sememe_prediction | 52 | 3 years ago | 1 | mit | Python | |||||
Codes for Lexical Sememe Prediction via Word Embeddings and Matrix Factorization (IJCAI 2017). | ||||||||||
Tadw | 50 | 9 months ago | gpl-3.0 | Python | ||||||
An implementation of "Network Representation Learning with Rich Text Information" (IJCAI '15). | ||||||||||
Text_embedding | 48 | 3 years ago | mit | Python | ||||||
utility class for building/evaluating document representations |
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).
The python module dependencies are:
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.
We adapted the weighted matrix factorization (WMF) implementation from content_wmf repository.
See example notebooks in src/
.