LOCA

This is the official code with preprocessed datasets for the WSDM 2021 paper: Local Collaborative Autoencoders.

The slides can be found here.

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Dataset

Dataset	# Users	# Items	# Ratings	Sparsity	Concentration
ML10M	69,878	10,677	10,000,054	98.66%	48.04%
ML20M	138,493	26,744	20,000,263	99.46%	66.43%
AMusic	4,964	11,797	97,439	99.83%	14.93%
AGames	13,063	17,408	236,415	99.90%	16.40%
Yelp	25,677	25,815	731,671	99.89%	22.78%

We use five public benchmark datasets: MovieLens 10M (ML10M), MovieLens 20M (ML20M), Amazon Digital Music (AMusic), Amazon Video Games (AGames), and Yelp 2015 (Yelp) datasets. We convert all explicit ratings to binary values, whether the ratings are observed or missing. For the MovieLens datasets, we did not modify the original data except for binarization. For the Amazon datasets, We removed users with ratings less than 10, resulting in 97,439 (Music) and 236,415 (Games) ratings. For the Yelp dataset, we pre-processed Yelp 2015 challenge dataset as in Fast Matrix Factorization for Online Recommendation with Implicit Feedback , where users and items with less than 10 interactions are removed.

You can get the original datasets from the following links:

Movielens: https://grouplens.org/datasets/movielens/

Amazon Review Data: https://nijianmo.github.io/amazon/

Yelp 2015: https://github.com/hexiangnan/sigir16-eals/tree/master/data

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Basic Usage

• Change the experimental settings in main_config.cfg and the model hyperparameters in model_config.
  
• Run main.py to train and test models.
  
• Command line arguments are also acceptable with the same naming in configuration files. (Both main/model config)

For example: python main.py --model_name MultVAE --lr 0.001

Running LOCA

Before running LOCA, you need (1) user embeddings to find local communities and (2) the global model to cover users who are not considered by local models.

1. Run single MultVAE and EASE to get user embedding vectors and the global model:

python main.py --model_name MultVAE and python main.py --model_name EASE

2. Train LOCA with the specific backbone model:

python main.py --model_name LOCA_VAE and python main.py --model_name LOCA_EASE

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Requirements

• Python 3
• Torch 1.5

Citation

Please cite our papaer:

@inproceedings{DBLP:conf/wsdm/ChoiJLL21,
  author    = {Minjin Choi and
               Yoonki Jeong and
               Joonseok Lee and
               Jongwuk Lee},
  title     = {Local Collaborative Autoencoders},
  booktitle = {{WSDM} '21, The Fourteenth {ACM} International Conference on Web Search
               and Data Mining, Virtual Event, Israel, March 8-12, 2021},
  pages     = {734--742},
  publisher = {{ACM}},
  year      = {2021},
  url       = {https://doi.org/10.1145/3437963.3441808},
  doi       = {10.1145/3437963.3441808},
  timestamp = {Wed, 07 Apr 2021 16:17:44 +0200},
  biburl    = {https://dblp.org/rec/conf/wsdm/ChoiJLL21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}