SETS: search enhanced by topic segmentation

  • Authors:
  • Mayank Bawa;Gurmeet Singh Manku;Prabhakar Raghavan

  • Affiliations:
  • Stanford University, Stanford, CA;Stanford University, Stanford, CA;Verity Inc.

  • Venue:
  • Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
  • Year:
  • 2003

Quantified Score

Hi-index 0.01

Visualization

Abstract

We present SETS, an architecture for efficient search in peer-to-peer networks, building upon ideas drawn from machine learning and social network theory. The key idea is to arrange participating sites in a topic-segmented overlay topology in which most connections are short-distance, connecting pairs of sites with similar content. Topically focused sets of sites are then joined together into a single network by long-distance links. Queries are matched and routed to only the topically closest regions. We discuss a variety of design issues and tradeoffs that an implementor of SETS would face. We show that SETS is efficient in network traffic and query processing load.