Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Pipei Huang"'
Publikováno v:
AAAI
Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms. Generally speaking, two essential modules named "Click-Through Rate Prediction" (\textit{CTR}) and "Conversion Rate Prediction" (\textit{CV
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::747726f028ff26870bf73fdf82dfc700
https://doi.org/10.1609/aaai.v33i01.3301338
https://doi.org/10.1609/aaai.v33i01.3301338
Publikováno v:
ICDE
In recent years, embedding models based on skip-gram algorithm have been widely applied to real-world recommendation systems (RSs). When designing embedding-based methods for recommendation at Taobao, there are three main challenges: scalability, spa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc1261f232e85be1da781105522ad77b
https://doi.org/10.1109/icde48307.2020.00148
https://doi.org/10.1109/icde48307.2020.00148
Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dimensional vectors, which are then fed on to MLP for final recommendatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d34086b49d068cf7b4d59c9277bcc49b
http://arxiv.org/abs/1905.06874
http://arxiv.org/abs/1905.06874
Autor:
Qiwei Chen, Guoliang Kang, Mengmeng Wu, Huan Zhao, Chao Li, Wei Li, Dik Lun Lee, Pipei Huang, Zhiyuan Liu, Yuchi Xu
Publikováno v:
CIKM
Industrial recommender systems have embraced deep learning algorithms for building intelligent systems to make accurate recommendations. At its core, deep learning offers powerful ability for learning representations from data, especially for user an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bce99834d2ee0cbf6a51afe1b43d8c24
http://arxiv.org/abs/1904.08030
http://arxiv.org/abs/1904.08030
Autor:
Xin Guo, Binqiang Zhao, Jiaming Xu, Fei Sun, Andreas Pfadler, Pipei Huang, Chao Li, Huan Zhao, Wen Chen, Cheng Guo
Publikováno v:
KDD
Increasing demand for fashion recommendation raises a lot of challenges for online shopping platforms and fashion communities. In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff2cd1dcab8173f84080a30149197ada
Publikováno v:
Pattern Recognition Letters. 33:568-579
Transfer learning aims at adapting a classifier trained on one domain with adequate labeled samples to a new domain where samples are from a different distribution and have no class labels. In this paper, we explore the transfer learning problems wit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0daf132c510aa79481af95b881dbb7f0
https://doi.org/10.1016/j.patrec.2011.11.023
https://doi.org/10.1016/j.patrec.2011.11.023
Publikováno v:
Pattern Recognition Letters. 31:1693-1700
Boosting has become one of the state-of-the-art techniques in many supervised learning and semi-supervised learning applications. In this paper, we develop a novel boosting algorithm, MTBoost, for multi-task learning problem. Many previous multi-task
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::53a2827465816fe4aa98da6317a0aa31
https://doi.org/10.1016/j.patrec.2010.05.019
https://doi.org/10.1016/j.patrec.2010.05.019
Publikováno v:
IFAC Proceedings Volumes. 41:10075-10080
In automatic control and its related applications, many problems can be formulated as the regression estimation problem. In this paper, we construct a nonlinear regression model by using kernels as basis functions in a dictionary and applying the L 1
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32c7fd264a0cd3a4a6f1d1557e2213b7
https://doi.org/10.3182/20080706-5-kr-1001.01705
https://doi.org/10.3182/20080706-5-kr-1001.01705
Publikováno v:
IFAC Proceedings Volumes. 41:6088-6093
Traditional learning algorithm uses only labeled data for training. However, labeled examples are often difficult or time consuming to obtain since they require substantial labeling efforts from humans. On the other hand, unlabeled data are often rel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8a5eccff154233b7ba9c10068b7e3d2
https://doi.org/10.3182/20080706-5-kr-1001.01028
https://doi.org/10.3182/20080706-5-kr-1001.01028
Autor:
Aaron F. Bobick, Kim Wallen, Rahul Sawhney, Pipei Huang, Shiyin Qin, Daniel Walker, Tucker Balch
Publikováno v:
IROS
We present a method to locate animals in video based on their reported positions using noisy and biased measurements from a radio frequency identification (RFID) system. The system uses a kernel regression method to learn a mapping from reported X, Y
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66d66b21b67e5d5ef59d0debb65d9b80
https://doi.org/10.1109/iros.2012.6385495
https://doi.org/10.1109/iros.2012.6385495