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

[1]

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

Digital Library

[2]

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

Digital Library

[3]

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.

Digital Library

[4]

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.

[5]

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.

Digital Library

[6]

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.

[7]

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

[8]

Janez Demšar. 2006. Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7 (2006), 1--30.

Digital Library

[9]

Michael Ekstrand and John Riedl. 2012. When Recommenders Fail: Predicting Recommender Failure for Algorithm Selection. ACM RecSys (2012), 233--236.

Digital Library

[10]

Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011. MyMediaLite. In ACM RecSys. 305--308.

Digital Library

[11]

Josephine Griffith, Colm O'Riordan, and Humphrey Sorensen. 2012. Investigations into user rating information and predictive accuracy in a collaborative filtering domain. In ACM Symposium on Applied Computing. 937--942.

Digital Library

[12]

Eyke Hüllermeier, Johannes Fürnkranz, Weiwei Cheng, and Klaus Brinker. 2008. Label ranking by learning pairwise preferences. Artificial Intelligence 172, 16-17 (2008), 1897--1916.

Digital Library

[13]

Pawel Matuszykand Myra Spiliopoulou. 2014. Predicting the Performance of Collaborative Filtering. In Web Intelligence, Mining and Semantics. 38:1--38:6.

Digital Library

[14]

Fábio Pinto, Carlos Soares, and João Mendes-Moreira. 2016. Towards automatic generation of Metafeatures. In PAKDD. 215--226.

[15]

John Rice. 1976. The Algorithm Selection Problem. Advances in Computers 15 (1976), 65--118.

[16]

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

[17]

Shankar Vembu and Thomas Gärtner. 2010. Label ranking algorithms: A survey. In Preference Learning. 45--64.

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

Recommendations

Metalearning for Context-aware Filtering: Selection of Tensor Factorization Algorithms
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

This work addresses the problem of selecting Tensor Factorization algorithms for the Context-aware Filtering recommendation task using a metalearning approach. The most important challenge of applying metalearning on new problems is the development of ...
Ranking-Oriented Collaborative Filtering: A Listwise Approach

Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, ...
CF4CF: recommending collaborative filtering algorithms using collaborative filtering
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

As Collaborative Filtering becomes increasingly important in both academia and industry recommendation solutions, it also becomes imperative to study the algorithm selection task in this domain. This problem aims at finding automatic solutions which ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

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.

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2018

Check for updates

Author Tags

Qualifiers

Poster

Funding Sources

Fundação para a Ciência e Tecnologia

Conference

SAC 2018

Sponsor:

SIGAPP

SAC 2018: Symposium on Applied Computing

April 9 - 13, 2018

Pau, France

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
97
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)1

Reflects downloads up to 24 Nov 2024

Other Metrics

View Author Metrics

Citations

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

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents