|
|
|
Spring 2007 Seminar Series
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
OPERATIONS RESEARCH CENTER
SPRING 2007 SEMINAR SERIES
DATE: Thursday, March 8, 2007
LOCATION: E40-298
TIME: 4:15pm
Reception immediately following in the Philip M. Morse Reading Room, E40-106
SPEAKER:
Paat Rusmevichientong
TITLE
An Adaptive Algorithm for Selecting Profitable Keywords for Search-Based
Advertising Services
ABSTRACT
Increases in online search activities have spurred the growth of
search-based advertising services offered by search engines.
These services enable companies to promote their products to
consumers based on their search queries. In most search-based
advertising services, a company sets a daily budget, selects
a set of keywords, determines a bid price for each keyword, and
designates an ad associated with each selected keyword. When
a consumer searches for one of the selected keywords, search
engines then display the ads associated with the highest bids
for that keyword on the search result page. A company whose ad
is displayed pays the search engine only when the consumer clicks
on the ad. If the company's spending has exceeded its daily budget,
however, its ads will not be displayed. With millions of available
keywords and a highly uncertain clickthru rate associated with
the ad for each keyword, identifying the most profitable set
of keywords given the daily budget constraint becomes challenging
for companies wishing to promote their goods and services via
search-based advertising.
Motivated by these challenges, we formulate
a model of keyword selection in search-based advertising services.
We develop an algorithm that adaptively identifies the set of
keywords to bid on based on historical performance. The algorithm
prioritizes keywords based on a prefix ordering -- sorting of
keywords in a descending order of profit-to-cost ratio (or bang-per-buck).
We show that the average expected profit generated by the algorithm
converges to near-optimal profits. Furthermore, the convergence
rate is independent of the number of keywords and scales gracefully
with the problem's parameters. Extensive numerical simulations
show that our algorithm outperforms existing methods, increasing
profits by about 7%. We also explore extensions to current search-based
advertising services and indicate how to adapt our algorithm
to these settings.
This is joint work with David P. Williamson
at Cornell University.
Back to Seminar Series schedule page |
|
|
|
|