refs_advertising.bib

@inproceedings{yan2020ads,
    author = {Yan, Jinyun and Xu, Zhiyuan and Tiwana, Birjodh and Chatterjee, Shaunak},
    title = {Ads Allocation in Feed via Constrained Optimization},
    year = {2020},
    isbn = {9781450379984},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://dl.acm.org/doi/pdf/10.1145/3394486.3403391},
    doi = {10.1145/3394486.3403391},
    booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
    pages = {3386–3394},
    numpages = {9},
    keywords = {user feedback modeling, social networks, constrained optimization, computational advertising},
    location = {Virtual Event, CA, USA},
    series = {KDD '20}
}
@inproceedings{he2014practical,
  title={Practical lessons from predicting clicks on ads at facebook},
  author={He, Xinran and Pan, Junfeng and Jin, Ou and Xu, Tianbing and Liu, Bo and Xu, Tao and Shi, Yanxin and Atallah, Antoine and Herbrich, Ralf and Bowers, Stuart and others},
  booktitle={Proceedings of the Eighth International Workshop on Data Mining for Online Advertising},
  pages={1--9},
  year={2014}
}
@article{guo2017deepfm,
  title={DeepFM: a factorization-machine based neural network for CTR prediction},
  author={Guo, Huifeng and Tang, Ruiming and Ye, Yunming and Li, Zhenguo and He, Xiuqiang},
  journal={arXiv preprint arXiv:1703.04247},
  year={2017}
}
@inproceedings{zhou2018deep,
  title={Deep interest network for click-through rate prediction},
  author={Zhou, Guorui and Zhu, Xiaoqiang and Song, Chenru and Fan, Ying and Zhu, Han and Ma, Xiao and Yan, Yanghui and Jin, Junqi and Li, Han and Gai, Kun},
  booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={1059--1068},
  year={2018}
}
@incollection{wang2017deep,
  title={Deep \& cross network for ad click predictions},
  author={Wang, Ruoxi and Fu, Bin and Fu, Gang and Wang, Mingliang},
  booktitle={Proceedings of the ADKDD'17},
  pages={1--7},
  year={2017}
}
@inproceedings{pi2019practice,
  title={Practice on long sequential user behavior modeling for click-through rate prediction},
  author={Pi, Qi and Bian, Weijie and Zhou, Guorui and Zhu, Xiaoqiang and Gai, Kun},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={2671--2679},
  year={2019}
}
@article{zhang2020general,
    title={General-Purpose User Embeddings based on Mobile App Usage},
    author={Zhang, Junqi and Bai, Bing and Lin, Ye and Liang, Jian and Bai, Kun and Wang, Fei},
    journal={arXiv preprint arXiv:2005.13303},
    url={https://arxiv.org/pdf/2005.13303.pdf},
    year={2020}
}
@inproceedings{dewet2019finding,
  title={Finding Users Who Act Alike: Transfer Learning for Expanding Advertiser Audiences},
  author={deWet, Stephanie and Ou, Jiafan},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={2251--2259},
  year={2019}
}
@inproceedings{liu2019real,
  title={Real-time Attention Based Look-alike Model for Recommender System},
  author={Liu, Yudan and Ge, Kaikai and Zhang, Xu and Lin, Leyu},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={2765--2773},
  year={2019}
}
@article{xiao2017attentional,
  title={Attentional factorization machines: Learning the weight of feature interactions via attention networks},
  author={Xiao, Jun and Ye, Hao and He, Xiangnan and Zhang, Hanwang and Wu, Fei and Chua, Tat-Seng},
  journal={arXiv preprint arXiv:1708.04617},
  year={2017}
}
@inproceedings{pan2018field,
  title={Field-weighted factorization machines for click-through rate prediction in display advertising},
  author={Pan, Junwei and Xu, Jian and Ruiz, Alfonso Lobos and Zhao, Wenliang and Pan, Shengjun and Sun, Yu and Lu, Quan},
  booktitle={Proceedings of the 2018 World Wide Web Conference},
  pages={1349--1357},
  year={2018}
}