Hierarchical Latent Relation Modeling for Collaborative Metric Learning

This repository provides Python code to reproduce experiments from our paper:

V-A. Tran, G. Salha-Galvan, R. Hennequin and M. Moussallam. Hierarchical Latent Relation Modeling for Collaborative Metric Learning. In: Proceedings of the 15th ACM Conference on Recommender Systems (RecSys 2021), September 2021.

Environment

• python 3.6.9
• tensorflow 1.15
• numpy 1.18.1
• scipy 1.6.2
• sklearn 0.22.2
• pandas 1.0.1
• toolz 0.11.1
• implicit 0.4.4

Datasets

The following datasets are considered in our work that could be easily downloaded from Internet and put in exp/data directory

• Movielens 20M (https://grouplens.org/datasets/movielens/20m/)
• Echonest (http://millionsongdataset.com/sites/default/files/challenge/train_triplets.txt.zip)
• Yelp (https://www.kaggle.com/yelp-dataset/yelp-dataset)
• Amazon Book (https://jmcauley.ucsd.edu/data/amazon/)

Hyperparameters

Best hyperparameters that we found through grid-search for each model on each dataset are reported in the corresponding configuration file in configs directory

Experiments

All experiment scripts for train / evaluation of our models and other baselines described in the paper could be found in scripts directory.

You could do the following steps to run experiment:

1. Download data and put it into exp/data directory. For example exp/data/ml-20m for Movielens 20M.
2. Change data path and interaction file name in configuration file (for example configs/mvlens/10-core/cml/*.json).
3. Run experiment script (that contains both train and evaluation commands) in scripts directory