Zobrazeno 1 - 8
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pro vyhledávání: '"Bao, Wentian"'
Autor:
Bao, Wentian, Liu, Hu, Zheng, Kai, Zhang, Chao, Zhang, Shunyu, Yu, Enyun, Ou, Wenwu, Song, Yang
Personalized search has been extensively studied in various applications, including web search, e-commerce, social networks, etc. With the soaring popularity of short-video platforms, exemplified by TikTok and Kuaishou, the question arises: can perso
Externí odkaz:
http://arxiv.org/abs/2409.11281
Autor:
Li, Wuchao, Huang, Rui, Zhao, Haijun, Liu, Chi, Zheng, Kai, Liu, Qi, Mou, Na, Zhou, Guorui, Lian, Defu, Song, Yang, Bao, Wentian, Yu, Enyun, Ou, Wenwu
Sequential Recommendation (SR) plays a pivotal role in recommender systems by tailoring recommendations to user preferences based on their non-stationary historical interactions. Achieving high-quality performance in SR requires attention to both ite
Externí odkaz:
http://arxiv.org/abs/2408.12153
Scale-calibrated ranking systems are ubiquitous in real-world applications nowadays, which pursue accurate ranking quality and calibrated probabilistic predictions simultaneously. For instance, in the advertising ranking system, the predicted click-t
Externí odkaz:
http://arxiv.org/abs/2406.08010
Autor:
Fan, Zhifang, Ou, Dan, Gu, Yulong, Fu, Bairan, Li, Xiang, Bao, Wentian, Dai, Xin-Yu, Zeng, Xiaoyi, Zhuang, Tao, Liu, Qingwen
Modeling user's historical feedback is essential for Click-Through Rate Prediction in personalized search and recommendation. Existing methods usually only model users' positive feedback information such as click sequences which neglects the context
Externí odkaz:
http://arxiv.org/abs/2203.15542
Conversion Rate (\emph{CVR}) prediction in modern industrial e-commerce platforms is becoming increasingly important, which directly contributes to the final revenue. In order to address the well-known sample selection bias (\emph{SSB}) and data spar
Externí odkaz:
http://arxiv.org/abs/2104.09713
Autor:
Zhang, Wenhao, Bao, Wentian, Liu, Xiao-Yang, Yang, Keping, Lin, Quan, Wen, Hong, Ramezani, Ramin
Post-click conversion rate (CVR) estimation is a critical task in e-commerce recommender systems. This task is deemed quite challenging under the industrial setting with two major issues: 1) selection bias caused by user self-selection, and 2) data s
Externí odkaz:
http://arxiv.org/abs/1910.09337
Recommender system, as an essential part of modern e-commerce, consists of two fundamental modules, namely Click-Through Rate (CTR) and Conversion Rate (CVR) prediction. While CVR has a direct impact on the purchasing volume, its prediction is well-k
Externí odkaz:
http://arxiv.org/abs/1910.07099
Finding hot topics in scholarly fields can help researchers to keep up with the latest concepts, trends, and inventions in their field of interest. Due to the rarity of complete large-scale scholarly data, earlier studies target this problem based on
Externí odkaz:
http://arxiv.org/abs/1710.06637