Behavioral classification on the click graph

ABSTRACT

A bipartite query-URL graph, where an edge indicates that a document was clicked for a query, is a useful construct for finding groups of related queries and URLs. Here we use this behavior graph for classification. We choose a click graph sampled from two weeks of image search activity, and the task of "adult" filtering: identifying content in the graph that is inappropriate for minors. We show how to perform classification using random walks on this graph, and two methods for estimating classifier parameters.

REFERENCES

• 1: Nick Craswell , Martin Szummer, Random walks on the click graph, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277784]
• 2: Szummer and T. Jaakkola. Partially labeled classification with Markov random walks. In Advances in Neural Information Processing Systems (NIPS), vol. 14, pages 945--952. MIT Press, Jan. 2002.
• 3: Tishby and N. Slonim. Data clustering by Markovian relaxation and the information bottleneck method. In Advances in Neural Information Processing Systems NIPS volume 13, pages 640--646, 2001.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

General Terms:
Algorithms, Experimentation

Keywords:
classification, click data