activity
20192022
most cited: Field-matrixed Factorization Machines for Recommender Systems

89 citations · 158 across the 7 of their papers we have counts for

collaborators

9 papers

cs.GT20222 cited

Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions

Wei Zhang, Yanjun Han, Zhengyuan Zhou +2

With the advent and increasing consolidation of e-commerce, digital advertising has very recently replaced traditional advertising as the main marketing force in the economy. In th…

stat.ML2021

Mid-flight Forecasting for CPA Lines in Online Advertising

Hao He, Tian Zhou, Lihua Ren +2

For Verizon MediaDemand Side Platform(DSP), forecasting of ad campaign performance not only feeds key information to the optimization server to allow the system to operate on a hig…

cs.GT202129 cited

An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions

Tian Zhou, Hao He, Shengjun Pan +9

Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference bet…

cs.IR202189 cited

: Field-matrixed Factorization Machines for Recommender Systems

Yang Sun, Junwei Pan, Alex Zhang +1

Click-through rate (CTR) prediction plays a critical role in recommender systems and online advertising. The data used in these applications are multi-field categorical data, where…

cs.GT202012 cited

Bid Shading by Win-Rate Estimation and Surplus Maximization

Shengjun Pan, Brendan Kitts, Tian Zhou +8

This paper describes a new win-rate based bid shading algorithm (WR) that does not rely on the minimum-bid-to-win feedback from a Sell-Side Platform (SSP). The method uses a modifi…

cs.GT202023 cited

Bid Shading in The Brave New World of First-Price Auctions

Djordje Gligorijevic, Tian Zhou, Bharatbhushan Shetty +4

Online auctions play a central role in online advertising, and are one of the main reasons for the industry's scalability and growth. With great changes in how auctions are being o…