Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Lee, Kuang‐chih"'
Ads demand forecasting for Walmart's ad products plays a critical role in enabling effective resource planning, allocation, and management of ads performance. In this paper, we introduce a comprehensive demand forecasting system that tackles hierarch
Externí odkaz:
http://arxiv.org/abs/2412.14718
Autor:
Zhou, Jianghong, Du, Weizhi, Rokon, Md Omar Faruk, Wang, Zhaodong, Xu, Jiaxuan, Shah, Isha, Lee, Kuang-chih, Wen, Musen
The rapid proliferation of e-commerce platforms accentuates the need for advanced search and retrieval systems to foster a superior user experience. Central to this endeavor is the precise extraction of product attributes from customer queries, enabl
Externí odkaz:
http://arxiv.org/abs/2312.06684
In the dynamic field of eCommerce, the quality and comprehensiveness of product descriptions are pivotal for enhancing search visibility and customer engagement. Effective product descriptions can address the 'cold start' problem, align with market t
Externí odkaz:
http://arxiv.org/abs/2310.18357
Publikováno v:
Workshop on Decision Intelligence and Analytics for Online Marketplaces, KDD 2023
Online retailers often use third-party demand-side-platforms (DSPs) to conduct offsite advertising and reach shoppers across the Internet on behalf of their advertisers. The process involves the retailer participating in instant auctions with real-ti
Externí odkaz:
http://arxiv.org/abs/2306.10476
Autor:
Wu, Di, Chen, Cheng, Chen, Xiujun, Pan, Junwei, Yang, Xun, Tan, Qing, Xu, Jian, Lee, Kuang-Chih
In online display advertising, guaranteed contracts and real-time bidding (RTB) are two major ways to sell impressions for a publisher. For large publishers, simultaneously selling impressions through both guaranteed contracts and in-house RTB has be
Externí odkaz:
http://arxiv.org/abs/2203.07073
Nowadays, deep learning models are widely adopted in web-scale applications such as recommender systems, and online advertising. In these applications, embedding learning of categorical features is crucial to the success of deep learning models. In t
Externí odkaz:
http://arxiv.org/abs/2109.02471
Embedding learning for categorical features is crucial for the deep learning-based recommendation models (DLRMs). Each feature value is mapped to an embedding vector via an embedding learning process. Conventional methods configure a fixed and unifor
Externí odkaz:
http://arxiv.org/abs/2108.11513
Autor:
Ma, Xu, Wang, Pengjie, Zhao, Hui, Liu, Shaoguo, Zhao, Chuhan, Lin, Wei, Lee, Kuang-Chih, Xu, Jian, Zheng, Bo
In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted. Such architecture usually consists of matching, pre-ranking, ranking, and re-ranking stages. In the pre-ranking stage, vector-pro
Externí odkaz:
http://arxiv.org/abs/2105.07706
Autor:
Du, Chao, Gao, Zhifeng, Yuan, Shuo, Gao, Lining, Li, Ziyan, Zeng, Yifan, Zhu, Xiaoqiang, Xu, Jian, Gai, Kun, Lee, Kuang-chih
Modern online advertising systems inevitably rely on personalization methods, such as click-through rate (CTR) prediction. Recent progress in CTR prediction enjoys the rich representation capabilities of deep learning and achieves great success in la
Externí odkaz:
http://arxiv.org/abs/2012.02298
In recent years, deep neural network is widely used in machine learning. The multi-class classification problem is a class of important problem in machine learning. However, in order to solve those types of multi-class classification problems effecti
Externí odkaz:
http://arxiv.org/abs/1806.02507