126 citations · 494 across the 31 of their papers we have counts for
10 papers · 1 filter
Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching
Zongtao Liu, Bin Ma, Quan Liu +2
Digital advertising is a critical part of many e-commerce platforms such as Taobao and Amazon. While in recent years a lot of attention has been drawn to the consumer side includin…
Binary Code based Hash Embedding for Web-scale Applications
Bencheng Yan, Pengjie Wang, Jinquan Liu +4
Nowadays, deep learning models are widely adopted in web-scale applications such as recommender systems, and online advertising. In these applications, embedding learning of catego…
Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer
Bencheng Yan, Pengjie Wang, Kai Zhang +4
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 embeddi…
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
Xiangyu Liu, Chuan Yu, Zhilin Zhang +10
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mecha…
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search
Ziyu Guan, Hongchang Wu, Qingyu Cao +7
Bid optimization for online advertising from single advertiser's perspective has been thoroughly investigated in both academic research and industrial practice. However, existing w…
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Chao Wen, Miao Xu, Zhilin Zhang +12
In online advertising, auto-bidding has become an essential tool for advertisers to optimize their preferred ad performance metrics by simply expressing high-level campaign objecti…
Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction
Feng Li, Bencheng Yan, Qingqing Long +4
Cross features play an important role in click-through rate (CTR) prediction. Most of the existing methods adopt a DNN-based model to capture the cross features in an implicit mann…
Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach
Xu Ma, Pengjie Wang, Hui Zhao +6
In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted. Such architecture usually consists of matching, pre-ranking…
Computation Resource Allocation Solution in Recommender Systems
Xun Yang, Yunli Wang, Cheng Chen +4
Recommender systems rely heavily on increasing computation resources to improve their business goal. By deploying computation-intensive models and algorithms, these systems are abl…
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng +7
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform…