Selective Hypertext Induced Topic Search

• Amit Awekar, North Carolina State University, USA
• Pabitra Mitra, Indian Institute of Technology, India
• Jaewoo Kang, North Carolina State University, USA

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Track: Posters

We address the problem of answering broad-topic queries on the World Wide Web. We present a link based analysis algorithm SelHITS which is an improvement over Kleinberg's HITS algorithm, based on two features: Novel approach to calculate hub and authority values, and Selective expansion of the root set. We introduce the concept of virtual links to exploit the latent information in the hyperlinked environment. Based on this concept, we propose a novel approach to calculate hub and authority values. We present a selective expansion method which avoids topic drift and provides results consistent with only one interpretation of the query even if the query is ambiguous. Initial experimental evaluation and user feedback show that our algorithm indeed distills the most important and relevant pages for broad-topic queries. We also infer that there exists a uniform notion of quality of search results within users.