Efficient Document Indexing Using Pivot Tree

Gaurav Singh Benjamin Piwowarski 1
1 BD - Bases de Données
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : We present a novel method for efficiently searching top-k neighbors for documents represented in high dimensional space of terms based on the cosine similarity. Mostly, documents are stored as bag-of-words tf-idf representation. One of the most used ways of computing similarity between a pair of documents is cosine similarity between the vector representations, but cosine similarity is not a metric distance measure as it doesn't follow triangle inequality, therefore most metric searching methods can not be applied directly. We propose an efficient method for indexing documents using a pivot tree that leads to efficient retrieval. We also study the relation between precision and efficiency for the proposed method and compare it with a state of the art in the area of document searching based on inner product.
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https://hal.sorbonne-universite.fr/hal-01358681
Contributor : Benjamin Piwowarski <>
Submitted on : Thursday, September 1, 2016 - 11:25:37 AM
Last modification on : Thursday, March 21, 2019 - 1:21:22 PM

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  • HAL Id : hal-01358681, version 1
  • ARXIV : 1605.06693

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Gaurav Singh, Benjamin Piwowarski. Efficient Document Indexing Using Pivot Tree. [Research Report] Sorbonne Universités, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606. 2016. ⟨hal-01358681⟩

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