Topic-based user segmentation for online advertising with latent dirichlet allocation

Authors:
Songgao Tu;Chaojun Lu
Affiliations:
Deptartment of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Deptartment of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Year:
2010

Citing 8
Cited 0

A vector space model for automatic indexing

Communications of the ACM
Latent dirichlet allocation

The Journal of Machine Learning Research
Demographic prediction based on user's browsing behavior

Proceedings of the 16th international conference on World Wide Web
A semantic approach to contextual advertising

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
How much can behavioral targeting help online advertising?

Proceedings of the 18th international conference on World wide web
Probabilistic latent semantic user segmentation for behavioral targeted advertising

Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Expectation-propagation for the generative aspect model

UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Web user segmentation based on a mixture of factor analyzers

EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

Behavioral Targeting (BT), as a useful technique to deliver the most appropriate advertisements to the most interested users by analyzing the user behaviors pattern, has gained considerable attention in online advertising market in recent year. A main task of BT is how to automatically segment web users for ads delivery, and good user segmentation may greatly improve the effectiveness of their campaigns and increase the ad click-through rate (CTR). Classical user segmentation methods, however, rarely take the semantics of user behaviors into consideration and can not mine the user behavioral pattern as properly as should be expected. In this paper, we propose an innovative approach based on the effective semantic analysis algorithm Latent Dirichlet Allocation (LDA) to attack this problem. Comparisons with other three baseline algorithms through experiments have confirmed that the proposed approach can increase effectiveness of user segmentation significantly.