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Feature-Oriented Analysis of User Profile Completion Problem

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Advances in Information Retrieval (ECIR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10193))

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Abstract

We study the user profile completion and enrichment problem, where the goal is to estimate the unknown values of user profiles. We investigate how the type of the features (categorical or continuous) suggests the use of a specific approach for this task. In particular, in this context, we validate the hypothesis that a classification method like K-nearest neighbor search fits better for categorical features and matrix factorization methods such as Non-negative Matrix Factorization perform superior on continuous features. We study different variants of K-nearest neighbor search (with different metrics) and demonstrate how they perform in different settings. Moreover, we investigate the impact of shifting the variables on the quality of (non-negative) factorization and the prediction error. We validate our methods via extensive experiments on real-world datasets and, finally, based on the results and observations, we discuss a hybrid approach to accomplish this task.

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Notes

  1. 1.

    The unknown elements might be filled by a default value before computing the pairwise distances.

  2. 2.

    Matrix factorization methods often require \(\mathcal O(N^3)\) or \(\mathcal O(N^2 \log N)\) runtime for training (and then they need to do matrix multiplication for estimation), whereas the runtime of different variants of K-NN is \(\mathcal O(N)\) (more precisely \(\mathcal O(KN|\mathcal M|)\) for \(|\mathcal M|\) unknown elements).

  3. 3.

    In our experiments, we did not observe a significant improvement when applying K-NN variants on the NMF results, instead of the original dataset.

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Authors and Affiliations

  1. Xerox Research Centre Europe - XRCE, Meylan, France

    Morteza Haghir Chehreghani

Authors
  1. Morteza Haghir Chehreghani
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Correspondence to Morteza Haghir Chehreghani .

Editor information

Editors and Affiliations

  1. University of Glasgow , Glasgow, United Kingdom

    Joemon M Jose

  2. TU Delft - EWI/ST/WIS , Delft, The Netherlands

    Claudia Hauff

  3. Middle East Technical University , Ankara, Turkey

    Ismail Sengor Altıngovde

  4. Open University , Milton Keynes, United Kingdom

    Dawei Song

  5. Signal Media , London, United Kingdom

    Dyaa Albakour

  6. Toronto, Canada

    Stuart Watt

  7. JohnTait.net Ltd. and BCS IRSG , Sunderland, United Kingdom

    John Tait

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Haghir Chehreghani, M. (2017). Feature-Oriented Analysis of User Profile Completion Problem. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_24

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  • DOI: https://doi.org/10.1007/978-3-319-56608-5_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56607-8

  • Online ISBN: 978-3-319-56608-5

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