A peer-selection algorithm for information retrieval

Yosi Mass*, Yehoshua Sagiv, Michal Shmueli-Scheuer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A novel method for creating collection summaries is developed, and a fully decentralized peer-selection algorithm is described. This algorithm finds the most promising peers for answering a given query. Specifically, peers publish per-term synopses of their documents. The synopses of a peer for a given term are divided into score intervals and for each interval, a KMV (K Minimal Values) synopsis of its documents is created. The synopses are used to effectively rank peers by their relevance to a multi-term query The proposed approach is verified by experiments on a large real-world dataset. In particular, two collections were created from this dataset, each with a different number of peers. Compared to the state-of-the-art approaches, the proposed method is effective and efficient even when documents are randomly distributed among peers.

Original languageEnglish
Title of host publicationCIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops
Pages1601-1604
Number of pages4
DOIs
StatePublished - 2010
Event19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10 - Toronto, ON, Canada
Duration: 26 Oct 201030 Oct 2010

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
Country/TerritoryCanada
CityToronto, ON
Period26/10/1030/10/10

Keywords

  • KMV
  • P2P search
  • Top-k

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