mirror of
https://github.com/openmlsys/openmlsys-zh.git
synced 2026-02-11 06:05:51 +08:00
59 lines
3.4 KiB
BibTeX
59 lines
3.4 KiB
BibTeX
@ARTICLE{2020tkde_li,
|
|
author={Li, Xiao-Hui and Cao, Caleb Chen and Shi, Yuhan and Bai, Wei and Gao, Han and Qiu, Luyu and Wang, Cong and Gao, Yuanyuan and Zhang, Shenjia and Xue, Xun and Chen, Lei},
|
|
journal={IEEE Transactions on Knowledge and Data Engineering},
|
|
title={A Survey of Data-driven and Knowledge-aware eXplainable AI},
|
|
year={2020},
|
|
volume={},
|
|
number={},
|
|
pages={1-1},
|
|
doi={10.1109/TKDE.2020.2983930}
|
|
}
|
|
|
|
@article{erhan2009visualizing,
|
|
title={Visualizing higher-layer features of a deep network},
|
|
author={Erhan, Dumitru and Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
|
|
journal={University of Montreal},
|
|
volume={1341},
|
|
number={3},
|
|
pages={1},
|
|
year={2009}
|
|
}
|
|
|
|
@misc{kim2018interpretability,
|
|
title={Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)},
|
|
author={Been Kim and Martin Wattenberg and Justin Gilmer and Carrie Cai and James Wexler and Fernanda Viegas and Rory Sayres},
|
|
year={2018},
|
|
eprint={1711.11279},
|
|
archivePrefix={arXiv},
|
|
primaryClass={stat.ML}
|
|
}
|
|
|
|
@article{riedl2019human,
|
|
title={Human-centered artificial intelligence and machine learning},
|
|
author={Riedl, Mark O.},
|
|
journal={Human Behavior and Emerging Technologies},
|
|
volume={1},
|
|
number={1},
|
|
pages={33--36},
|
|
year={2019},
|
|
publisher={Wiley Online Library}
|
|
|
|
}
|
|
|
|
@inproceedings{10.1145/2988450.2988454,
|
|
author = {Cheng, Heng-Tze and Koc, Levent and Harmsen, Jeremiah and Shaked, Tal and Chandra, Tushar and Aradhye, Hrishi and Anderson, Glen and Corrado, Greg and Chai, Wei and Ispir, Mustafa and Anil, Rohan and Haque, Zakaria and Hong, Lichan and Jain, Vihan and Liu, Xiaobing and Shah, Hemal},
|
|
title = {Wide & Deep Learning for Recommender Systems},
|
|
year = {2016},
|
|
isbn = {9781450347952},
|
|
publisher = {Association for Computing Machinery},
|
|
address = {New York, NY, USA},
|
|
url = {https://doi.org/10.1145/2988450.2988454},
|
|
doi = {10.1145/2988450.2988454},
|
|
abstract = {Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.},
|
|
booktitle = {Proceedings of the 1st Workshop on Deep Learning for Recommender Systems},
|
|
pages = {7-10},
|
|
numpages = {4},
|
|
keywords = {Recommender Systems, Wide & Deep Learning},
|
|
location = {Boston, MA, USA},
|
|
series = {DLRS 2016}
|
|
} |