Zobrazeno 1 - 10
of 149
pro vyhledávání: '"Kuang-Chih lee"'
Publikováno v:
MIS Quarterly; Dec2020, Vol. 44 Issue 4, p1459-1492, 34p, 1 Chart, 1 Graph
Autor:
Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic, Kuang-Chih Lee, Kun Liu, Wei Liu, Linsey Pang, Vladan Radosavljevic, Suju Rajan, Kexin Xie
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9c1b5271259feb86a19cb0a890391472
https://doi.org/10.1145/3534678.3542920
https://doi.org/10.1145/3534678.3542920
Publikováno v:
CIKM
Post-click conversion rate (CVR) estimation is a crucial task in online advertising and recommendation systems. To address the sample selection bias problem in traditional CVR models trained in click space, recent studies perform entire space multi-t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cca21b178864edfe2a0b3f40f5e45dbb
https://doi.org/10.1145/3459637.3482061
https://doi.org/10.1145/3459637.3482061
Autor:
Zhe Wang, Guorui Zhou, Ruiming Tang, Xiaoqiang Zhu, Biye Jiang, Kan Ren, Weinan Zhang, Qingyao Ai, Kuang-Chih Lee
Publikováno v:
KDD
Recently, we have witnessed that deep learning-based approaches has been widely applied to empower many internet-scale applications. However, the data in these internet-scale applications are high dimensional and extremely sparse, which makes it diff
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5e321ea67d9afb1b6a7415be57147c8
https://doi.org/10.1145/3447548.3469444
https://doi.org/10.1145/3447548.3469444
Autor:
Abraham Bagherjeiran, Nemanja Djuric, Kuang-Chih Lee, Suju Rajan, Mihaljo Grbovic, Vladan Radosavljevic, Kun Liu
Publikováno v:
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.
The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds and more recently compounded with the complex user's path to purchase across devices
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3e798cdfb65c22bd61452d465271de48
https://doi.org/10.1145/3447548.3469486
https://doi.org/10.1145/3447548.3469486
Autor:
Kuang-Chih Lee, Hui Zhao, Xu Ma, Bo Zheng, Pengjie Wang, Wei Lin, Jian Xu, Shaoguo Liu, Chuhan Zhao
Publikováno v:
SIGIR
In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted. Such architecture usually consists of matching, pre-ranking, ranking, and re-ranking stages. In the pre-ranking stage, vector-pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::425cc2f038c20c06ea8f141a618460f9
http://arxiv.org/abs/2105.07706
http://arxiv.org/abs/2105.07706
Publikováno v:
CIKM
Embedding learning for categorical features is crucial for the deep learning-based recommendation models (DLRMs). Each feature value is mapped to an embedding vector via an embedding learning process. Conventional methods configure a fixed and unifor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04d2d170f2baaa78266d22d89830dfbf
Publikováno v:
SSRN Electronic Journal.
We develop an optimization model and corresponding algorithm for the management of a demand-side platform (DSP), whereby the DSP acquires valuable impressions for its advertiser clients in a real-time bidding environment. We propose a highly flexible
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c7c92c09e671d7739470f6cc90f1da9
https://doi.org/10.2139/ssrn.3841306
https://doi.org/10.2139/ssrn.3841306
Publikováno v:
ICDM
In many applications, a parsimonious model is often preferred for better interpretability and predictive performance. Online algorithms have been studied extensively for building such models in big data and fast evolving environments, with a prominen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c50ea785871ace2b4778f01b68334aae
https://doi.org/10.1109/icdm50108.2020.00023
https://doi.org/10.1109/icdm50108.2020.00023
Autor:
Zhifeng Gao, Chao Du, Jian Xu, Kun Gai, Lining Gao, Xiaoqiang Zhu, Kuang-Chih Lee, Yifan Zeng, Shuo Yuan, Ziyan Li
Publikováno v:
KDD
Modern online advertising systems inevitably rely on personalization methods, such as click-through rate (CTR) prediction. Recent progress in CTR prediction enjoys the rich representation capabilities of deep learning and achieves great success in la
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55cb939eb8813d17b3a1d8e4578cf3c3