Codes for our SIGIR-2019 paper:
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
This repo includes an example for training Meta-Embedding upon a deepFM model on the binarized MovieLens-1M dataset. The dataset is preprocessed and splitted already.
Requirements: Python 3 and TensorFlow.
@inproceedings{pan2019warm,
author = {Pan, Feiyang and Li, Shuokai and Ao, Xiang and Tang, Pingzhong and He, Qing},
title = {Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings},
booktitle = {Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
series = {SIGIR'19},
year = {2019},
isbn = {978-1-4503-6172-9},
location = {Paris, France},
pages = {695--704},
numpages = {10},
url = {http:https://doi.acm.org/10.1145/3331184.3331268},
doi = {10.1145/3331184.3331268},
acmid = {3331268},
publisher = {ACM},
address = {New York, NY, USA},
}