Search

3 results

8 - Advertising on the Web
Jure Leskovec, Stanford University, California, Anand Rajaraman, Jeffrey David Ullman, Stanford University, California
Book:

Mining of Massive Datasets

Published online:

16 April 2020

Print publication:

09 January 2020, pp 282-306
- Chapter
- - Get access
    
    Check if you have access via personal or institutional login
    
    Log in Register
- Export citation
Summary

By far the most lucrative venue for on-line advertising has been search, and much of the effectiveness of search advertising comes from the “adwords” model of matching search queries to advertisements. We shall therefore devote much of this chapter to algorithms for optimizing the way this assignment is done. The algorithms used are of an unusual type; they are greedy and they are “on-line” in a particular technical sense to be discussed. We shall therefore digress to discuss these two algorithmic issues – greediness and on-line algorithms – in general, before tackling the adwords problem. A second interesting on-line advertising problem involves selecting items to advertise at an on-line store. This problem involves “collaborative filtering,” where we try to find customers with similar behavior in order to suggest they buy things that similar customers have bought. This subject will be treated in Section 9.3.

Matchings and the variance of Lipschitz functions
Franck Barthe, Neil O'Connell
Journal:

ESAIM: Probability and Statistics / Volume 13 / July 2009

Published online by Cambridge University Press:

22 September 2009, pp. 400-408

Print publication:

July 2009
- Article
- - Get access
    
    Check if you have access via personal or institutional login
    
    Log in Register
- Export citation
We are interested in the rate function of the moderate deviation principle for the two-sample matching problem. This is related to the determination of 1-Lipschitzfunctions with maximal variance. We give an exact solution for random variables which havenormal law, or are uniformly distributed on the Euclidean ball.

Stochastic analysis of partitioning algorithms for matching problems
Part of
- Limit theorems
Ludger Rüschendorf, Gernot Sachs
Journal:

Journal of Applied Probability / Volume 37 / Issue 2 / June 2000

Published online by Cambridge University Press:

14 July 2016, pp. 494-503

Print publication:

June 2000
- Article
- - Get access
    
    Check if you have access via personal or institutional login
    
    Log in Register
- Export citation
Partitioning algorithms for the Euclidean matching and for the semi-matching problem in the plane are introduced and analysed. The algorithms are analogues of Karp's well-known dissection algorithm for the travelling salesman problem. The algorithms are proved to run in time nlogn and to approximate the optimal matching in the probabilistic sense. The analysis is based on the techniques developed in Karp (1977) and on the limit theorem of Redmond and Yukich (1993) for quasiadditive functionals.