Sensitive webpage classification for content advertising

ABSTRACT

Online advertising has been a popular topic in recent years. In this paper, we address one of the important problems in online advertising, i.e., how to detect whether a publisher webpage contains sensitive content and is appropriate for showing advertisement(s) on it.

We take a webpage classification approach to solve this problem. First we design a unique sensitive content taxonomy. Then we adopt an iterative training data collection and classifier building approach, to build a hierarchical classifier which can classify webpages into one of the nodes in the sensitive content taxonomy. The experimental result show that using this approach, we are able to build a unique sensitive content classifier with decent accuracy while only requiring limited amount of human labeling effort.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES

Additional Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.2 Types of Systems
          Subjects: Decision support (e.g., MIS)

General Terms:
Algorithms

Keywords:
SVM, active learning, hierarchical text classification, logistic regression