Top-k rertrieval using facility location analysis

Guido Zuccon, Leif Azzopardi, Dell Zhang, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

33 Citations (Scopus)


The top-k retrieval problem aims to find the optimal set of k documents from a number of relevant documents given the user’s query. The key issue is to balance the relevance and diversity of the top-k search results. In this paper, we address this problem using Facility Location Analysis taken from Operations Research, where the locations of facilities are optimally chosen according to some criteria. We show how this analysis technique is a generalization of state-of-the-art retrieval models for diversification (such as the Modern Portfolio Theory for Information Retrieval), which treat the top-k search results like “obnoxious facilities” that should be dispersed as far as possible from each other. However, Facility Location Analysis suggests that the top-k search results could be treated like “desirable facilities” to be placed as close as possible to their customers. This leads to a new top-k retrieval model where the best representatives of the relevant documents are selected. In a series of experiments conducted on two TREC diversity collections, we show that significant improvements can be made over the current state-of-the-art through this alternative treatment of the top-k retrieval problem.
Original languageEnglish
Title of host publicationProceedings of the 34th European Conference on Advances in Information Retrieval
Place of PublicationBerlin, Heidelberg
Number of pages12
ISBN (Print)978-3-642-28996-5
Publication statusPublished - 2012
Externally publishedYes

Publication series

NameLecture Notes in Computer Science


  • information storage
  • database management
  • information retrieval


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