Learning While Bidding in Real Time Auctions with Multiple Item Types and Unknown Price Distribution
Loading...
Date
2024-05-14
Authors
Boreiri, Amirhossein
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
A Real-Time Bidding (RTB) network is a real-time auction market, primarily used for
advertising space sales. Within this environment, clients participate by bidding on preferred
items and subsequently purchasing them upon winning. This thesis addresses the
problem of optimal real-time bidding within a second-price Vickrey auction setting, where
the distribution of prices is unknown. Our focus centers on second-price auction mechanisms,
which offers unique properties that enable the development of compelling algorithms.
We introduce the concept of a demand-side platform (DSP), acting as an intermediary representing
clients in the auction market. With no prior knowledge of typical prices, the DSP
must determine optimal bidding strategies for each item and distribute won items among
clients to fulfill their contracts while minimizing expenses. When the distribution of the
prices of items is known, this optimal bidding problem can be solved by classic convex
optimization algorithms such as ADMM. However, market properties may vary over time,
and access to competitor behavior or bidding information is limited. Consequently, the
DSP must continually update its information about the price distribution, while adapting
bidding estimations in real-time. Our primary contribution lies in devising efficient
online optimization algorithms that accurately find the optimal bids. To tackle this, we
employ tools from convex optimization analysis, including duality, along with stochastic
optimization algorithms, notably stochastic approximation. Moreover, techniques such as
projection and penalty term methods are utilized to enhance algorithm performance.
Description
Keywords
Real-time Bidding, Auction Theory, Convex Optimization, Stochastic Approximation, Online Learning