Zobrazeno 1 - 10
of 395
pro vyhledávání: '"Han Shuguang"'
Autor:
Chen, Kang, Zhang, Qingheng, Lian, Chengbao, Ji, Yixin, Liu, Xuwei, Han, Shuguang, Wu, Guoqiang, Huang, Fei, Chen, Jufeng
Unlike professional Business-to-Consumer (B2C) e-commerce platforms (e.g., Amazon), Consumer-to-Consumer (C2C) platforms (e.g., Facebook marketplace) are mainly targeting individual sellers who usually lack sufficient experience in e-commerce. Indivi
Externí odkaz:
http://arxiv.org/abs/2410.16977
Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges
Externí odkaz:
http://arxiv.org/abs/2403.06747
Autor:
Guan, Jianyu, Yin, Zongming, Zhang, Tianyi, Chen, Leihui, Zhang, Yin, Huang, Fei, Chen, Jufeng, Han, Shuguang
In recent years, the recommendation content on e-commerce platforms has become increasingly rich -- a single user feed may contain multiple entities, such as selling products, short videos, and content posts. To deal with the multi-entity recommendat
Externí odkaz:
http://arxiv.org/abs/2402.19101
Autor:
Gui, Xiaoqiang, Cheng, Yueyao, Sheng, Xiang-Rong, Zhao, Yunfeng, Yu, Guoxian, Han, Shuguang, Jiang, Yuning, Xu, Jian, Zheng, Bo
In machine learning systems, privileged features refer to the features that are available during offline training but inaccessible for online serving. Previous studies have recognized the importance of privileged features and explored ways to tackle
Externí odkaz:
http://arxiv.org/abs/2312.08727
Autor:
Zhao, Yunfeng, Yan, Xu, Gui, Xiaoqiang, Han, Shuguang, Sheng, Xiang-Rong, Yu, Guoxian, Chen, Jufeng, Xu, Zhao, Zheng, Bo
Conversion rate (CVR) prediction is an essential task for large-scale e-commerce platforms. However, refund behaviors frequently occur after conversion in online shopping systems, which drives us to pay attention to effective conversion for building
Externí odkaz:
http://arxiv.org/abs/2308.04768
Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user feedback. Since ad impressions are purposely selected by the model itse
Externí odkaz:
http://arxiv.org/abs/2306.03527
Autor:
Zhao, Zhishan, Gao, Jingyue, Zhang, Yu, Han, Shuguang, Lou, Siyuan, Sheng, Xiang-Rong, Wang, Zhe, Zhu, Han, Jiang, Yuning, Xu, Jian, Zheng, Bo
Cascading architecture has been widely adopted in large-scale advertising systems to balance efficiency and effectiveness. In this architecture, the pre-ranking model is expected to be a lightweight approximation of the ranking model, which handles m
Externí odkaz:
http://arxiv.org/abs/2306.03516
Autor:
Chan, Zhangming, Zhang, Yu, Han, Shuguang, Bai, Yong, Sheng, Xiang-Rong, Lou, Siyuan, Hu, Jiacen, Liu, Baolin, Jiang, Yuning, Xu, Jian, Zheng, Bo
Conversion rate (CVR) prediction is one of the core components in online recommender systems, and various approaches have been proposed to obtain accurate and well-calibrated CVR estimation. However, we observe that a well-trained CVR prediction mode
Externí odkaz:
http://arxiv.org/abs/2305.12837
Autor:
Zhang, Zhao-Yu, Sheng, Xiang-Rong, Zhang, Yujing, Jiang, Biye, Han, Shuguang, Deng, Hongbo, Zheng, Bo
Deep learning techniques have been applied widely in industrial recommendation systems. However, far less attention has been paid to the overfitting problem of models in recommendation systems, which, on the contrary, is recognized as a critical issu
Externí odkaz:
http://arxiv.org/abs/2209.06053
Autor:
Zhang, Yujing, Chan, Zhangming, Xu, Shuhao, Bian, Weijie, Han, Shuguang, Deng, Hongbo, Zheng, Bo
An industrial recommender system generally presents a hybrid list that contains results from multiple subsystems. In practice, each subsystem is optimized with its own feedback data to avoid the disturbance among different subsystems. However, we arg
Externí odkaz:
http://arxiv.org/abs/2208.10174