Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Zhao, Xiwei"'
Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that have prevailed for decades. On one hand, IDRec often faces significant
Externí odkaz:
http://arxiv.org/abs/2403.10049
Model evolution and constant availability of data are two common phenomena in large-scale real-world machine learning applications, e.g. ads and recommendation systems. To adapt, the real-world system typically retrain with all available data and onl
Externí odkaz:
http://arxiv.org/abs/2307.01206
Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data observed by the CTR model typically arrives as a stream. Streaming dat
Externí odkaz:
http://arxiv.org/abs/2304.09062
Autor:
Xu, Han, Qi, Hao, Wang, Kunyao, Wang, Pei, Zhang, Guowei, Liu, Congcong, Jin, Junsheng, Zhao, Xiwei, Lin, Zhangang, Hu, Jinghe, Shao, Jingping
Traditional online advertising systems for sponsored search follow a cascade paradigm with retrieval, pre-ranking,ranking, respectively. Constrained by strict requirements on online inference efficiency, it tend to be difficult to deploy useful but c
Externí odkaz:
http://arxiv.org/abs/2206.12893
Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass of tasks and achieved fabulous results, such as link prediction, node classification, graph classification and so on. Generally, for a certain node in
Externí odkaz:
http://arxiv.org/abs/2205.05348
Autor:
Zhang, Yinan, Wang, Pei, Liu, Congcong, Zhao, Xiwei, Qi, Hao, He, Jie, Jin, Junsheng, Peng, Changping, Lin, Zhangang, Shao, Jingping
Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn informative user and item representations by performing embedding propagatio
Externí odkaz:
http://arxiv.org/abs/2204.03827
Autor:
Liu, Congcong, Li, Yuejiang, Teng, Fei, Zhao, Xiwei, Peng, Changping, Lin, Zhangang, Hu, Jinghe, Shao, Jingping
Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying. In practical application, CTR models often serve with high-speed user-generated data streams, whose underlying distribution
Externí odkaz:
http://arxiv.org/abs/2204.05101
Autor:
Liu, Congcong, Li, Yuejiang, Zhu, Jian, Zhao, Xiwei, Peng, Changping, Lin, Zhangang, Shao, Jingping
Click-through rate (CTR) Prediction is of great importance in real-world online ads systems. One challenge for the CTR prediction task is to capture the real interest of users from their clicked items, which is inherently biased by presented position
Externí odkaz:
http://arxiv.org/abs/2204.00270
Autor:
Zhu, Jian, Liu, Congcong, Wang, Pei, Zhao, Xiwei, Chen, Guangpeng, Jin, Junsheng, Peng, Changping, Lin, Zhangang, Shao, Jingping
Learning to capture feature relations effectively and efficiently is essential in click-through rate (CTR) prediction of modern recommendation systems. Most existing CTR prediction methods model such relations either through tedious manually-designed
Externí odkaz:
http://arxiv.org/abs/2111.04983
Autor:
Liu, Hu, Lu, Jing, Zhao, Xiwei, Xu, Sulong, Peng, Hao, Liu, Yutong, Zhang, Zehua, Li, Jian, Jin, Junsheng, Bao, Yongjun, Yan, Weipeng
Click-through rate (CTR) prediction is one of the fundamental tasks for e-commerce search engines. As search becomes more personalized, it is necessary to capture the user interest from rich behavior data. Existing user behavior modeling algorithms d
Externí odkaz:
http://arxiv.org/abs/2010.00985