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NearBucket-LSH: Efficient Similarity Search in P2P Networks

  • Naama KrausEmail author
  • David Carmel
  • Idit Keidar
  • Meni Orenbach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9939)

Abstract

We present NearBucket-LSH, an effective algorithm for similarity search in large-scale distributed online social networks organized as peer-to-peer overlays. As communication is a dominant consideration in distributed systems, we focus on minimizing the network cost while guaranteeing good search quality. Our algorithm is based on Locality Sensitive Hashing (LSH), which limits the search to collections of objects, called buckets, that have a high probability to be similar to the query. More specifically, NearBucket-LSH employs an LSH extension that searches in near buckets, and improves search quality but also significantly increases the network cost. We decrease the network cost by considering the internals of both LSH and the P2P overlay, and harnessing their properties to our needs. We show that our NearBucket-LSH increases search quality for a given network cost compared to previous art. In many cases, the search quality increases by more than \(50\,\%\).

Keywords

Hash Function Success Probability Online Social Network Cosine Similarity Network Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Naama Kraus is grateful to the Hasso-Plattner-Institut (HPI) for the scholarship for doctoral studies.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Naama Kraus
    • 1
    Email author
  • David Carmel
    • 2
  • Idit Keidar
    • 1
    • 2
  • Meni Orenbach
    • 1
  1. 1.Viterbi EE TechnionHaifaIsrael
  2. 2.Yahoo ResearchHaifaIsrael

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