Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Hu, Jinghe"'
Autor:
Wang, Huimu, Li, Mingming, Miao, Dadong, Wang, Songlin, Tang, Guoyu, Liu, Lin, Xu, Sulong, Hu, Jinghe
Re-ranking is a process of rearranging ranking list to more effectively meet user demands by accounting for the interrelationships between items. Existing methods predominantly enhance the precision of search results, often at the expense of diversit
Externí odkaz:
http://arxiv.org/abs/2405.15521
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
Autor:
Song, Jinbo, Huang, Ruoran, Wang, Xinyang, Huang, Wei, Yu, Qian, Chen, Mingming, Yao, Yafei, Fan, Chaosheng, Peng, Changping, Lin, Zhangang, Hu, Jinghe, Shao, Jingping
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 4495-4499
Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, including matching, pre-ranking, ranking and re-ranking. As a critical b
Externí odkaz:
http://arxiv.org/abs/2310.08039
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:
Zhao, Xi, Feng, Wei, Zhang, Zheng, Lv, Jingjing, Zhu, Xin, Lin, Zhangang, Hu, Jinghe, Shao, Jingping
Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion. However, the segmentation process only considers each pixel independently, and the expansion proce
Externí odkaz:
http://arxiv.org/abs/2212.02340
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
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
Designing an e-commerce recommender system that serves hundreds of millions of active users is a daunting challenge. From a human vision perspective, there're two key factors that affect users' behaviors: items' attractiveness and their matching degr
Externí odkaz:
http://arxiv.org/abs/1709.00300
Autor:
Wang, Yu, Liu, Jiayi, Liu, Yuxiang, Hao, Jun, He, Yang, Hu, Jinghe, Yan, Weipeng P., Li, Mantian
We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information. The agent is based on an asynchronou
Externí odkaz:
http://arxiv.org/abs/1708.05565
With the transition from people's traditional `brick-and-mortar' shopping to online mobile shopping patterns in web 2.0 $\mathit{era}$, the recommender system plays a critical role in E-Commerce and E-Retails. This is especially true when designing t
Externí odkaz:
http://arxiv.org/abs/1708.03993