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Studying the Effect of Data Structures on the Efficiency of Collaborative Filtering Systems

Published: 14 June 2016 Publication History

Abstract

Recommender systems is an active research area where the major focus has been on how to improve the quality of generated recommendations, but less attention has been paid on how to do it in an efficient way. This aspect is increasingly important because the information to be considered by recommender systems is growing exponentially. In this paper we study how different data structures affect the performance of these systems. Our results with two public datasets provide relevant insights regarding the optimal data structures in terms of memory and time usages. Specifically, we show that classical data structures like Binary Search Trees and Red-Black Trees can beat more complex and popular alternatives like Hash Tables.

References

[1]
A. Bellogín, J. Wang, and P. Castells. Bridging memory-based collaborative filtering and text retrieval. Inf. Retr., 16(6):697--724, 2013.
[2]
Ò. Celma. Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer, 2010.
[3]
S. H. S. Chee, J. Han, and K. Wang. Rectree: An efficient collaborative filtering method. In Y. Kambayashi, W. Winiwarter, and M. Arikawa, editors, Data Warehousing and Knowledge Discovery, Third International Conference, DaWaK 2001, Munich, Germany, September 5-7, 2001, Proceedings, volume 2114 of Lecture Notes in Computer Science, pages 141--151. Springer, 2001.
[4]
T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms (3. ed.). MIT Press, 2009.
[5]
P. Falley. Categories of data structures. J. Comput. Sci. Coll., 23(1):147--153, Oct. 2007.
[6]
V. Formoso, D. Fernández, F. Cacheda, and V. Carneiro. Using rating matrix compression techniques to speed up collaborative recommendations. Inf. Retr., 16(6):680--696, 2013.
[7]
G. Jacobson. Space-efficient static trees and graphs. In 30th Annual Symposium on Foundations of Computer Science, Research Triangle Park, North Carolina, USA, 30 October - 1 November 1989, pages 549--554. IEEE Computer Society, 1989.
[8]
Y. Koren and R. M. Bell. Advances in collaborative filtering. In Ricci et al. {10}, pages 77--118.
[9]
X. Ning, C. Desrosiers, and G. Karypis. A comprehensive survey of neighborhood-based recommendation methods. In Ricci et al. {10}, pages 37--76.
[10]
F. Ricci, L. Rokach, and B. Shapira, editors. Recommender Systems Handbook. Springer, 2015.
[11]
S. Vargas, C. Macdonald, and I. Ounis. Analysing compression techniques for in-memory collaborative filtering. In P. Castells, editor, Poster Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015, Vienna, Austria, September 16, 2015., volume 1441 of CEUR Workshop Proceedings. CEUR-WS.org, 2015.
  1. Studying the Effect of Data Structures on the Efficiency of Collaborative Filtering Systems

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    CERI '16: Proceedings of the 4th Spanish Conference on Information Retrieval
    June 2016
    146 pages
    ISBN:9781450341417
    DOI:10.1145/2934732
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    • University of Granada: University of Granada

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 14 June 2016

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    Author Tags

    1. Collaborative Filtering
    2. Data Structures
    3. Efficiency

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    • Short-paper
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    • Refereed limited

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    CERI '16

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    CERI '16 Paper Acceptance Rate 18 of 27 submissions, 67%;
    Overall Acceptance Rate 36 of 51 submissions, 71%

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