bensonku / metaembedding

Codes for our SIGIR-2019 paper "Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings"

MetaEmbedding

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.

Bibtex

@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://doi.acm.org/10.1145/3331184.3331268},
 doi = {10.1145/3331184.3331268},
 acmid = {3331268},
 publisher = {ACM},
 address = {New York, NY, USA},
}