114 citations · 208 across the 16 of their papers we have counts for
17 papers
Bid Optimization for Offsite Display Ad Campaigns on eCommerce
Hangjian Li, Dong Xu, Konstantin Shmakov +2
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 proces…
Impression Allocation and Policy Search in Display Advertising
Di Wu, Cheng Chen, Xiujun Chen +5
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…
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…
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…
Large scale classification in deep neural network with Label Mapping
Qizhi Zhang, Kuang-Chih Lee, Hongying Bao +3
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 ord…
Optimal Bidding, Allocation and Budget Spending for a Demand Side Platform Under Many Auction Types
Alfonso Lobos, Paul Grigas, Zheng Wen +1
We develop a novel optimization model to maximize the profit of a Demand-Side Platform (DSP) while ensuring that the budget utilization preferences of the DSP's advertiser clients…
Category coding with neural network application
Qizhi Zhang, Kuang-chih Lee, Hongying Bao +2
In many applications of neural network, it is common to introduce huge amounts of input categorical features, as well as output labels. However, since the required network size sho…
Optimal Reserve Price for Online Ads Trading Based on Inventory Identification
Zhihui Xie, Kuang-Chih Lee, Liang Wang
The online ads trading platform plays a crucial role in connecting publishers and advertisers and generates tremendous value in facilitating the convenience of our lives. It has be…
Proceedings of the 2017 AdKDD & TargetAd Workshop
Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic +4
Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax, Nova Scotia, Canada.
Profit Maximization for Online Advertising Demand-Side Platforms
Paul Grigas, Alfonso Lobos, Zheng Wen +1
We develop an optimization model and corresponding algorithm for the management of a demand-side platform (DSP), whereby the DSP aims to maximize its own profit while acquiring val…
Distributed Flexible Nonlinear Tensor Factorization
Shandian Zhe, Kai Zhang, Pengyuan Wang +4
Tensor factorization is a powerful tool to analyse multi-way data. Compared with traditional multi-linear methods, nonlinear tensor factorization models are capable of capturing mo…
Lift-Based Bidding in Ad Selection
Jian Xu, Xuhui Shao, Jianjie Ma +3
Real-time bidding (RTB) has become one of the largest online advertising markets in the world. Today the bid price per ad impression is typically decided by the expected value of h…
Smart Pacing for Effective Online Ad Campaign Optimization
Jian Xu, Kuang-chih Lee, Wentong Li +2
In targeted online advertising, advertisers look for maximizing campaign performance under delivery constraint within budget schedule. Most of the advertisers typically prefer to i…
User Clustering in Online Advertising via Topic Models
Sahin Cem Geyik, Ali Dasdan, Kuang-Chih Lee
In the domain of online advertising, our aim is to serve the best ad to a user who visits a certain webpage, to maximize the chance of a desired action to be performed by this user…
Visual Sentiment Prediction with Deep Convolutional Neural Networks
Can Xu, Suleyman Cetintas, Kuang-Chih Lee +1
Images have become one of the most popular types of media through which users convey their emotions within online social networks. Although vast amount of research is devoted to se…
Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising
Kuang-Chih Lee, Ali Jalali, Ali Dasdan
Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for adverti…