Choice of Metrics used in Collaborative Filtering and their Impact on Recommender Systems

Serradilla García, Francisco and Bobadilla Sancho, Jesus and Sánchez, J.L. and Martínez Murciano, Eduardo (2008). Choice of Metrics used in Collaborative Filtering and their Impact on Recommender Systems. In: "2nd IEEE International Conference on Digital Ecosystems and Technologies", 26/02/2008-29/02/2008, Phitsanulok, Thailand. ISBN 978-1-4244-1489-5. pp. 432-436. https://doi.org/10.1109/DEST.2008.4635147.

Description

Title: Choice of Metrics used in Collaborative Filtering and their Impact on Recommender Systems
Author/s:
  • Serradilla García, Francisco
  • Bobadilla Sancho, Jesus
  • Sánchez, J.L.
  • Martínez Murciano, Eduardo
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2nd IEEE International Conference on Digital Ecosystems and Technologies
Event Dates: 26/02/2008-29/02/2008
Event Location: Phitsanulok, Thailand
Title of Book: Digital Ecosystems and Technologies, 2008. DEST 2008. 2nd IEEE International Conference on
Date: 30 September 2008
ISBN: 978-1-4244-1489-5
Subjects:
Freetext Keywords: Collaborative filtering, metrics, recommender systems, cosine, correlation
Faculty: E.U. de Informática (UPM)
Department: Sistemas Inteligentes Aplicados [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The capacity of recommender systems to make correct predictions is essentially determined by the quality and suitability of the collaborative filtering that implements them. The common memory-based metrics are Pearson correlation and cosine, however, their use is not always the most appropriate or sufficiently justified. In this paper, we analyze these two metrics together with the less common mean squared difference (MSD) to discover their advantages and drawbacks in very important aspects such as the impact when introducing different values of k-neighborhoods, minimization of the MAE error, capacity to carry out a sufficient number of predictions, percentage of correct and incorrect predictions and behavior when attempting to recommend the n-best items. The paper lists the results and practical conclusions that have been obtained after carrying out a comparative study of the metrics based on 135 experiments on the MovieLens database of 100,000 ratios.

More information

Item ID: 3170
DC Identifier: http://oa.upm.es/3170/
OAI Identifier: oai:oa.upm.es:3170
DOI: 10.1109/DEST.2008.4635147
Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04635147
Deposited by: Memoria Investigacion
Deposited on: 31 May 2010 08:33
Last Modified: 04 Mar 2015 16:31
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