Imagine and Imitate: Cost-Effective Bidding under Partially Observable Price Landscapes

Abstract

Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidding strategy for real-time bidding is constrained by at least three aspects: cost-effectiveness, the dynamic nature of market prices, and the issue of missing bidding values. To address these challenges, we propose Imagine and Imitate Bidding (IIBidder), which includes Strategy Imitation and Imagination modules, to generate cost-effective bidding strategies under partially observable price landscapes. Experimental results on the iPinYou and YOYI datasets demonstrate that IIBidder reduces investment costs, optimizes bidding strategies, and improves future market price predictions.

Keywords

• real-time bidding
• imitation learning
• bidding landscape prediction
• partially observable Markov decision processes