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International Conference on Discovery Science

DS 2017: Discovery Science pp 189–203Cite as

Recommending Collaborative Filtering Algorithms Using Subsampling Landmarkers

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10558))

Abstract

Recommender Systems have become increasingly popular, propelling the emergence of several algorithms. As the number of algorithms grows, the selection of the most suitable algorithm for a new task becomes more complex. The development of new Recommender Systems would benefit from tools to support the selection of the most suitable algorithm. Metalearning has been used for similar purposes in other tasks, such as classification and regression. It learns predictive models to map characteristics of a dataset with the predictive performance obtained by a set of algorithms. For such, different types of characteristics have been proposed: statistical and/or information-theoretical, model-based and landmarkers. Recent studies argue that landmarkers are successful in selecting algorithms for different tasks. We propose a set of landmarkers for a Metalearning approach to the selection of Collaborative Filtering algorithms. The performance is compared with a state of the art systematic metafeatures approach using statistical and/or information-theoretical metafeatures. The results show that the metalevel accuracy performance using landmarkers is not statistically significantly better than the metafeatures obtained with a more traditional approach. Furthermore, the baselevel results obtained with the algorithms recommended using landmarkers are worse than the ones obtained with the other metafeatures. In summary, our results show that, contrary to the results obtained in other tasks, these landmarkers are not necessarily the best metafeatures for algorithm selection in Collaborative Filtering.

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Acknowledgements

This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 under the Portugal 2020 Partnership Agreement, and through the Portuguese National Innovation Agency (ANI) as a part of project «FASCOM | POCI-01-0247-FEDER-003506».

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Authors and Affiliations

  1. INESC-TEC/FEUP, Porto, Portugal

    Tiago Cunha & Carlos Soares

  2. ICMC - USP, São Carlos, São Paulo, Brazil

    André C. P. L. F. de Carvalho

Authors
  1. Tiago Cunha
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  2. Carlos Soares
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  3. André C. P. L. F. de Carvalho
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Correspondence to Tiago Cunha .

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Editors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Akihiro Yamamoto

  2. Hokkaido University, Sapporo, Japan

    Takuya Kida

  3. National Institute of Informatics, Tokyo, Japan

    Takeaki Uno

  4. Gakushuin University, Tokyo, Japan

    Tetsuji Kuboyama

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Cunha, T., Soares, C., de Carvalho, A.C.P.L.F. (2017). Recommending Collaborative Filtering Algorithms Using Subsampling Landmarkers. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds) Discovery Science. DS 2017. Lecture Notes in Computer Science(), vol 10558. Springer, Cham. https://doi.org/10.1007/978-3-319-67786-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-67786-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67785-9

  • Online ISBN: 978-3-319-67786-6

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