Optimal real-time bidding for display advertising

By Weinan Zhang, Shuai Yuan, Jun Wang. Knowledge Discovery and Data Mining, 2014

356 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d84eb36c688382d1b3af8201cfc3539db470ed8f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jun Wang
2. Weinan Zhang
3. Han Cai
4. Han Zhu
5. Shuai Yuan
6. Yanru Qu
7. Yong Yu
8. Kan Ren

Citations in the corpus

• Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting (2016, methodology): “Normally, these inferred user interest segments are then used as target restriction rules or as features leveraged in predicting users’ ad response [Zhang et al., 2014a], where those regression techniques introduced previously, e.g., logistic regression, matrix factorisation, boosted decision…”
• Optimized Cost per Click in Taobao Display Advertising (2017, background): “And lots of research [25, 24, 23, 26] has found effective and efficient bidding strategies to maximize unilateral economic surplus of a party, such as advertisers, consumers and intermediary platforms.”
• Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction (2016, background): “Sponsored search, contextual advertising, and the recently emerged real-time bidding (RTB) display advertising all heavily rely on the ability of learned models to predict ad click-through rates (CTR) [32,41].”
• A Marketplace for Data: An Algorithmic Solution (2018, background): “So it is possible for firms to make separate bids for different ad-spaces based on access to past performance information such as CTR (cf. Liu & Chen (2006); Zhang et al. (2014)).”
• Product-Based Neural Networks for User Response Prediction (2016, background): “This predicted probability indicates the user’s interest on the specific item such as a news article, a commercial item or an advertising post, which influences the subsequent decision making such as document ranking [2] and ad bidding [3].”
• Real-Time Bidding by Reinforcement Learning in Display Advertising (2017, methodology): “Machine based bid decision, i.e., to calculate the strategic amount that the advertiser would like to pay for an ad opportunity, constitutes a core component that drives the campaigns’ ROI [11, 32].”