Using Neighborhood Pre-computation to Increase Recommendation Efficiency

Vreixo Formoso, Diego Fernández, Fidel Cacheda, Victor Carneiro

2012

Abstract

Collaborative filtering is a very popular recommendation technique. Among the different approaches, the k- Nearest Neighbors algorithm stands out by its simplicity, and its good and explainable results. This algorithm bases its recommendations to a given user on the opinions of similar users. Thus, selecting those similar users is an important step in the recommendation, known as neighborhood selection. In real applications with millions of users and items, this step can be a serious performance bottleneck because of the huge number of operations needed. In this paper we study the possibility of pre-computing the neighbors in an offline step, in order to increase recommendation efficiency. We show how neighborhood pre-computation reduces the recommendation time by two orders of magnitude without a significant impact in recommendation precision.

Paper Citation

in Harvard Style

Formoso V., Fernández D., Cacheda F. and Carneiro V. (2012). Using Neighborhood Pre-computation to Increase Recommendation Efficiency . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 333-335. DOI: 10.5220/0004139703330335

in Bibtex Style

@conference{kdir12,
author={Vreixo Formoso and Diego Fernández and Fidel Cacheda and Victor Carneiro},
title={Using Neighborhood Pre-computation to Increase Recommendation Efficiency},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={333-335},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004139703330335},
isbn={978-989-8565-29-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Using Neighborhood Pre-computation to Increase Recommendation Efficiency
SN - 978-989-8565-29-7
AU - Formoso V.
AU - Fernández D.
AU - Cacheda F.
AU - Carneiro V.
PY - 2012
SP - 333
EP - 335
DO - 10.5220/0004139703330335