Learning to Target: What Works for Behavioral Targeting
Venue
CIKM '11, ACM, Glasgow, Scotland, UK (2011), pp. 1805-1814
Publication Year
2011
Authors
Sandeep Pandey, Mohamed Aly, Abraham Bagherjeiran, Andrew Hatch, Peter Ciccolo, Adwait Ratnaparkhi, Martin Zinkevich
BibTeX
Abstract
Understanding what interests and delights users is critical to effective behavioral
targeting, especially in information-poor contexts. As users interact with content
and advertising, their passive behavior can reveal their interests towards
advertising. Two issues are critical for building effective targeting methods: what
metric to optimize for and how to optimize. More specifically, we first attempt to
understand what the learning objective should be for behavioral targeting so as to
maximize advertiser’s performance. While most popular advertising methods optimize
for user clicks, as we will show, maximizing clicks does not necessarily imply
maximizing purchase activities or transactions, called conversions, which directly
translate to advertiser’s revenue. In this work we focus on conversions which makes
a more relevant metric but also the more challenging one. Second is the issue of
how to represent and combine the plethora of user activities such as search
queries, page views, ad clicks to perform the targeting. We investigate several
sources of user activities as well as methods for inferring conversion likelihood
given the activities. We also explore the role played by the temporal aspect of
user activities for targeting, e.g., how recent activities compare to the old ones.
Based on a rigorous offline empirical evaluation over 200 individual advertising
campaigns, we arrive at what we believe are best practices for behavioral
targeting. We deploy our approach over live user traffic to demonstrate its
superiority over existing state-of-the-art targeting methods.
