Update README.md

README.md

@@ -1,7 +1,7 @@
 # MetaEmbedding
 Codes for our SIGIR-2019 paper: 
 
-**[Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://arxiv.org/abs/1904.11547)**
+**[Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://dl.acm.org/citation.cfm?id=3331268)**
 
 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.
 
@@ -13,9 +13,17 @@ 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 (SIGIR '19)},
+ 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},
- doi = {10.1145/3331184.3331268}
+ 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},
 } 
 ```