An enhanced model for behavioral targeting in online advertising

2016 ◽  
Author(s):  
V M Radhika ◽  
Aiswarya Thottungal ◽  
M Abdul Nizar
Keyword(s):  
Online Advertising ◽  
Behavioral Targeting

Behavioral Targeting Online Advertising

2011 ◽  
pp. 213-232 ◽  
Author(s):  
Jun Yan ◽  
Dou Shen ◽  
Teresa Mah ◽  
Ning Liu ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Predictive Modeling ◽  
Rapid Growth ◽  
Online Advertising ◽  
Research Challenges ◽  
Behavioral Targeting ◽  
The Us ◽  
Collection Data ◽  
Complex Technology

With the rapid growth of the online advertising market, Behavioral Targeting (BT), which delivers advertisements to users based on understanding of their needs through their behaviors, is attracting more attention. The amount of spend on behaviorally targeted ad spending in the US is projected to reach $4.4 billion in 2012 (Hallerman, 2008). BT is a complex technology, which involves data collection, data mining, audience segmentation, contextual page analysis, predictive modeling and so on. This chapter gives an overview of Behavioral Targeting by introducing the Behavioral Targeting business, followed by classic BT research challenges and solution proposals. We will also point out BT research challenges which are currently under-explored in both industry and academia.

An Economic Analysis of Online Advertising Using Behavioral Targeting

MIS Quarterly ◽  
2014 ◽  
Vol 38 (2) ◽  
pp. 429-449 ◽  
Author(s):  
Jianqing Chen ◽  
◽  
Jan Stallaert ◽  
Keyword(s):  
Economic Analysis ◽  
Online Advertising ◽  
Behavioral Targeting

Audience Intelligence in Online Advertising

2011 ◽  
pp. 262-277
Author(s):  
Bin Wang
Keyword(s):  
Performance Measures ◽  
Online Advertising ◽  
End Users ◽  
Privacy Issue ◽  
Behavioral Targeting ◽  
The Relationship

This chapter introduces the fundamentals of audience intelligence’s important aspects. The goal is to present what are related to audience intelligence, how online audience intelligence could be done, and some representative methods. In this chapter, the author will first address the fundamentals of the audience intelligence, including the brief introduction of the online ad eco-system, the relationship between audience intelligence and existing online ad types, performance measures and the challenges in this field. Next, some classical methods of audience intelligence on end-users will be introduces, namely, demographic, geographic, behavioral targeting and online commercial intent (OCI) detection. Then, audience intelligence on advertisers will be presented. Finally, related topics of online advertising, such as the privacy issue, will be addressed.

Audience Intelligence in Online Advertising

Cyber Crime ◽  
2013 ◽  
pp. 1161-1176
Author(s):  
Bin Wang
Keyword(s):  
Performance Measures ◽  
Online Advertising ◽  
End Users ◽  
Privacy Issue ◽  
Behavioral Targeting ◽  
The Relationship

This chapter introduces the fundamentals of audience intelligence’s important aspects. The goal is to present what are related to audience intelligence, how online audience intelligence could be done, and some representative methods. In this chapter, the author will first address the fundamentals of the audience intelligence, including the brief introduction of the online ad eco-system, the relationship between audience intelligence and existing online ad types, performance measures and the challenges in this field. Next, some classical methods of audience intelligence on end-users will be introduces, namely, demographic, geographic, behavioral targeting and online commercial intent (OCI) detection. Then, audience intelligence on advertisers will be presented. Finally, related topics of online advertising, such as the privacy issue, will be addressed.

Online advertising, behavioral targeting, and privacy

2011 ◽  
Vol 54 (5) ◽  
pp. 25-27 ◽  
Author(s):  
Avi Goldfarb ◽  
Catherine E. Tucker
Keyword(s):  
Online Advertising ◽  
Behavioral Targeting

Intent-Based User Segmentation with Query Enhancement

2013 ◽  
Vol 3 (4) ◽  
pp. 1-17
Author(s):  
Wei Xiong ◽  
Michael Recce ◽  
Brook Wu
Keyword(s):  
Clustering Algorithm ◽  
Topic Model ◽  
Online Advertising ◽  
Empirical Evaluation ◽  
Enhancement Mechanism ◽  
User Classification ◽  
User Behaviors ◽  
Segmentation Strategy ◽  
Behavioral Targeting ◽  
User Query

With the rapid advancement of the internet, accurate prediction of user's online intent underlying their search queries has received increasing attention from online advertising community. This paper aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a query enhancement mechanism that augments user's queries by leveraging a user query log. The empirical evaluation demonstrates that the authors' methodology for query enhancement achieves greater improvement than the baseline models in both intent-based user classification and user segmentation. Different from traditional user segmentation methods, which take little semantics of user behaviors into consideration, the authors propose a novel user segmentation strategy by incorporating the query enhancement mechanism with a topic model to mine the relationships between users and their behaviors in order to segment users in a semantic manner. Comparing with a classical clustering algorithm, K-means, the experimental results indicate that the proposed user segmentation strategy helps improve behavioral targeting effectiveness significantly. This paper also proposes an alternative to define user's search intent for the evaluation purpose, in the case that the dataset is sanitized. This approach automatically labels users in a click graph, which are then used in training an intent-based user classifier.

Behavioral Targeting Online Advertising

Data Mining ◽  
2013 ◽  
pp. 1320-1338
Author(s):  
Jun Yan ◽  
Dou Shen ◽  
Teresa Mah ◽  
Ning Liu ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Predictive Modeling ◽  
Rapid Growth ◽  
Online Advertising ◽  
Research Challenges ◽  
Behavioral Targeting ◽  
The Us ◽  
Collection Data ◽  
Complex Technology

With the rapid growth of the online advertising market, Behavioral Targeting (BT), which delivers advertisements to users based on understanding of their needs through their behaviors, is attracting more attention. The amount of spend on behaviorally targeted ad spending in the US is projected to reach $4.4 billion in 2012 (Hallerman, 2008). BT is a complex technology, which involves data collection, data mining, audience segmentation, contextual page analysis, predictive modeling and so on. This chapter gives an overview of Behavioral Targeting by introducing the Behavioral Targeting business, followed by classic BT research challenges and solution proposals. We will also point out BT research challenges which are currently under-explored in both industry and academia.

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