poster

A label ranking approach for selecting rankings of collaborative filtering algorithms

Authors:

André C. P. L. F. de CarvalhoAuthors Info & Claims

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

Pages 1393 - 1395

https://doi.org/10.1145/3167132.3167418

Published: 09 April 2018 Publication History

Abstract

The large amount of Recommender System algorithms makes the selection of the most suitable algorithm for a new dataset a difficult task. Metalearning has been successfully used to deal with this problem. It works by mapping dataset characteristics with the predictive performance obtained by a set of algorithms. The models built on this data are capable of predicting the best algorithm for a new dataset. However, typical approaches try only to predict the best algorithm, overlooking the performance of others. This study focus on the use of Metalearning to select the best ranking of CF algorithms for a new recommendation dataset. The contribution lies in the formalization and experimental validation of using Label Ranking to select a ranked list of algorithms. The experimental procedure proves the superior performance of the proposed approach regarding both ranking accuracy and impact on the baselevel performance. Furthermore, it draws and compares the knowledge regarding metafeature importance for both classification and Label Ranking tasks in order to provide guidelines for the design of algorithms in the Recommender System community.

References

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Gediminas Adomavicius and Jingjing Zhang. 2012. Impact of data characteristics on recommender systems. ACM Information Systems 3, 1 (2012), 1--17.

Digital Library

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Pavel Brazdil, Christophe Giraud-Carrier, Carlos Soares, and Ricardo Vilalta. 2009. Metalearning: Applications to Data Mining (1 ed.). Springer.

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Pavel Brazdil, Carlos Soares, and Joaquim da Costa. 2003. Ranking Learning Algorithms : Using IBL and Meta-Learning on Accuracy and Time Results. Machine Learning 50, 3 (2003), 251--277.

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Tiago Cunha, Carlos Soares, and André C.P.L.F. de Carvalho. 2016. Selecting Collaborative Filtering algorithms using Metalearning. In European Conference on Machine Learning and Knowledge Discovery in Databases. 393--409.

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Tiago Cunha, Carlos Soares, and André C.P.L.F. de Carvalho. 2018. Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem. Information Sciences (2018), 128--144.

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Tiago Cunha, Carlos Soares, and Andre C. P. L. F. de Carvalho. 2017. Recommending Collaborative Filtering algorithms using subsampling landmarkers. In Discovery Science. 189--203.

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Cláudio Rebelo de Sá, Carlos Soares, Arno Knobbe, and Paulo Cortez. 2016. Label Ranking Forests. Expert Systems (2016).

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Carlos Soares. 2015. labelrank: Predicting Rankings of Labels. https://cran.r-project.org/package=labelrank

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Cited By

Suchithra MPai M(2022)Evaluating the performance of bagging-based k-nearest neighbor ensemble with the voting rule selection methodMultimedia Tools and Applications10.1007/s11042-022-12716-381:15(20741-20762)Online publication date: 12-Mar-2022
https://doi.org/10.1007/s11042-022-12716-3
Dery LShmueli E(2020)BoostLR: A Boosting-Based Learning Ensemble for Label Ranking TasksIEEE Access10.1109/ACCESS.2020.30267588(176023-176032)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3026758
Tornede AWever MHüllermeier E(2020)Extreme Algorithm Selection with Dyadic Feature RepresentationDiscovery Science10.1007/978-3-030-61527-7_21(309-324)Online publication date: 15-Oct-2020
https://doi.org/10.1007/978-3-030-61527-7_21
Show More Cited By

Index Terms

A label ranking approach for selecting rankings of collaborative filtering algorithms
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

April 2018

2327 pages

ISBN:9781450351911

DOI:10.1145/3167132

Conference Chairs:
Hisham M. Haddad
Kennesaw State University
,
Roger L. Wainwright
University of Tulsa
,
Richard Chbeir
University of Pau & Pays Adour, France

Copyright © 2018 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2018

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Fundação para a Ciência e Tecnologia

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SAC 2018

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SIGAPP

SAC 2018: Symposium on Applied Computing

April 9 - 13, 2018

Pau, France

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Suchithra MPai M(2022)Evaluating the performance of bagging-based k-nearest neighbor ensemble with the voting rule selection methodMultimedia Tools and Applications10.1007/s11042-022-12716-381:15(20741-20762)Online publication date: 12-Mar-2022
https://doi.org/10.1007/s11042-022-12716-3
Dery LShmueli E(2020)BoostLR: A Boosting-Based Learning Ensemble for Label Ranking TasksIEEE Access10.1109/ACCESS.2020.30267588(176023-176032)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3026758
Tornede AWever MHüllermeier E(2020)Extreme Algorithm Selection with Dyadic Feature RepresentationDiscovery Science10.1007/978-3-030-61527-7_21(309-324)Online publication date: 15-Oct-2020
https://doi.org/10.1007/978-3-030-61527-7_21
Cunha TSoares Cde Carvalho APera SEkstrand MAmatriain XO'Donovan J(2018)CF4CFProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240378(357-361)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240378
Cunha TSoares Cde Carvalho A(2018)CF4CF-META: Hybrid Collaborative Filtering Algorithm Selection FrameworkDiscovery Science10.1007/978-3-030-01771-2_8(114-128)Online publication date: 7-Oct-2018
https://doi.org/10.1007/978-3-030-01771-2_8

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