Efficiency-quality tradeoffs for vector score aggregation

  • Authors:

  • Pavan Kumar C. Singitham;Mahathi S. Mahabhashyam;Prabhakar Raghavan

  • Affiliations:

  • Stanford University, Stanford;Stanford University, Stanford;Verity Inc., Sunnyvale

  • Venue:
  • VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
  • Year:
  • 2004

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Abstract

Finding the l nearest neighbors to a query in a vector space is an important primitive in text and image retrieval. Here we study an extension of this problem with applications to XML and image retrieval: we have multiple vector spaces, and the query places a weight on each space. Match scores from the spaces are weighted by these weights to determine the overall match between each record and the query; this is a case of score aggregation. We study approximation algorithms that use a small fraction of the computation of exhaustive search through all records, while returning nearly the best matches. We focus on the tradeoff between the computation and the quality of the results. We develop two approaches to retrieval from such multiple vector spaces. The first is inspired by resource allocation. The second, inspired by computational geometry, combines the multiple vector spaces together with all possible query weights into a single larger space. While mathematically elegant, this abstraction is intractable for implementation. We therefore devise an approximation of this combined space. Experiments show that all our approaches (to varying extents) enable retrieval quality comparable to exhaustive search, while avoiding its heavy computational cost.