Paper Info
Title
A Markov chain model for integrating behavioral targeting into contextual advertising
Abstract
Both Contextual Advertising (CA) and Behavioral Targeting (BT) are playing important roles in online advertising market. Recently, the problem of how to integrate BT strategies into CA has attracted much attention from both industry and academia. However, to our best knowledge, few research works have been published to provide BT solutions in CA. In this paper, we propose a new notion of relevance between webpages and ads based on users' online click-through behaviors from BT's perspective. Compared with the classical behavior targeting method where only users' history interests are considered, we pay more attention to the click probability of ads from a webpage where the relevance between them is evaluated. Moreover, a combination model integrating behavioral relevance and contextual relevance for matching ads and webpags is presented. The model parameters are learnt from a dataset consisting of 200 webpages and 35,880 ads. Experimental results show that our integrated strategy indeed outperforms the strategies that only consider either behavioral relevance or contextual relevance. The best model achieves a 18.1% improvement in precision over single strategies.
Year
DOI
Venue
2009
10.1145/1592748.1592750
KDD Workshop on Data Mining and Audience Intelligence for Advertising
Keywords
Field
DocType
best model,markov chain model,contextual advertising,online click-through behavior,online advertising market,best knowledge,combination model,bt strategy,bt solution,model parameter,contextual relevance,behavioral relevance,online advertising,behavioral targeting
Contextual advertising,World Wide Web,Web page,Behavioral targeting,Computer science,Markov chain,Online advertising
Conference
Citations 
PageRank 
References 
11
0.66
8
Authors
6
Name
Order
Citations
PageRank
Ting Li1110.66
Ning Liu225315.62
Jun Yan3179885.25
Gang Wang452125.88
Fengshan Bai518420.65
Zheng Chen65019256.89