Efficient Document Indexing Using Pivot Tree
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.
Cite
@article{arxiv.1605.06693,
title = {Efficient Document Indexing Using Pivot Tree},
author = {Gaurav Singh and Benjamin Piwowarski},
journal= {arXiv preprint arXiv:1605.06693},
year = {2016}
}
Comments
6 Pages, 2 Figures