  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.
