tebesu / Collaborativememorynetwork
Collaborative Memory Network for Recommendation Systems, SIGIR 2018
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Collaborative Memory Network for Recommendation Systems
Implementation for
Travis Ebesu, Bin Shen, Yi Fang. Collaborative Memory Network for Recommendation Systems. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018.
https://arxiv.org/pdf/1804.10862.pdf
Bibtex
@inproceedings{Ebesu:2018:CMN:3209978.3209991,
author = {Ebesu, Travis and Shen, Bin and Fang, Yi},
title = {Collaborative Memory Network for Recommendation Systems},
booktitle = {The 41st International ACM SIGIR Conference on Research \&\#38; Development in Information Retrieval},
series = {SIGIR '18},
year = {2018},
isbn = {978-1-4503-5657-2},
location = {Ann Arbor, MI, USA},
pages = {515--524},
numpages = {10},
url = {http://doi.acm.org/10.1145/3209978.3209991},
doi = {10.1145/3209978.3209991},
acmid = {3209991},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {collaborative filtering, deep learning, memory networks},
}
Running Collaborative Memory Network
python train.py --gpu 0 --dataset data/citeulike-a.npz --pretrain pretrain/citeulike-a_e50.npz
To pretrain the model for initialization
python pretrain.py --gpu 0 --dataset data/citeulike-a.npz --output pretrain/citeulike-a_e50.npz
Requirements
- Python 3.6
- TensorFlow 1.4+
- dm-sonnet
Data Format
The structure of the data in the npz file is as follows:
train_data = [[user id, item id], ...]
test_data = {userid: (pos_id, [neg_id1, neg_id2, ...]), ...}
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