Abstract. Existing categorization algorithms deal with homogeneous Web objects, and consider interrelated objects as additional features when taking the interrelationships with other types of objects into account. However, focusing on any single aspect of the inter-object relationship is not sufficient to fully reveal the true categories of Web objects. In this paper, we propose a novel categorization algorithm, called the Iterative Reinforcement Categorization Algorithm (IRC), to exploit the full interrelationship between different types of Web objects on the Web, including Web pages and queries. IRC classifies the interrelated Web objects by iteratively reinforcing the individual classification results of different types of objects via their interrelationship. Experiments on a clickthrough-log dataset from the MSN search engine show that, in terms of the F 1 measure, IRC achieves a 26.4% improvement over a pure content-based classification method. It also achieves a 21% improvement over a query-metadata-based method, as well as a 16.4% improvement on F 1 measure over the well-known virtual document-based method. Our experiments show that IRC converges fast enough to be applicable to real world applications.
Xue et al. REF introduced an iterative reinforcement categorization (IRC) that exploits relationships between web objects to reinforce content based classification results by the propagation of the category from one object to related ones.
Reinforcing Web-object Categorization Through Interrelationships
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Computer Science