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User Profiling in Text-Based Recommender Systems Based on Distributed Word Representations

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Analysis of Images, Social Networks and Texts (AIST 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 661))

Abstract

We introduce a novel approach to constructing user profiles for recommender systems based on full-text items such as posts in a social network and implicit ratings (in the form of likes) that users give them. The profiles measure a user’s interest in various topics mined from the full texts of the items. As a result, we get a user profile that can be used for cold start recommendations for items, targeted advertisement, and other purposes. Our experiments show that the method performs on a level comparable with classical collaborative filtering algorithms while at the same time being a cold start approach, i.e., it does not use the likes of an item being recommended.

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Acknowledgements

This work was supported by the “Recommendation Systems with Automated User Profiling” project sponsored by Samsung and the Government of the Russian Federation grant 14.Z50.31.0030. We thank Dmitry Bugaichenko and the “Odnoklassniki” social network for providing us with the social network dataset with texts of posts and user likes and Alexander Panchenko and Nikolay Arefyev for the trained word2vec model along with its Russian-language training data.

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Authors and Affiliations

  1. Steklov Mathematical Institute at St. Petersburg, St. Petersburg, Russia

    Anton Alekseev & Sergey Nikolenko

  2. National Research University Higher School of Economics, St. Petersburg, Russia

    Sergey Nikolenko

  3. Deloitte Analytics Institute, Moscow, Russia

    Sergey Nikolenko

Authors
  1. Anton Alekseev
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  2. Sergey Nikolenko
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Corresponding author

Correspondence to Sergey Nikolenko .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovsky Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Mikhail Yu. Khachay

  3. Ural Federal University, Yekaterinbug, Russia

    Valeri G. Labunets

  4. Research Computing Center, Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Sergey I. Nikolenko

  6. Technische Universität Darmstadt, Darmstadt, Germany

    Alexander Panchenko

  7. Laboratory of Algorithms and Technologies for Networks Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  8. Dorodnicyn Computing Centre of Russian Academy of Sciences, Moscow, Russia

    Konstantin Vorontsov

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Alekseev, A., Nikolenko, S. (2017). User Profiling in Text-Based Recommender Systems Based on Distributed Word Representations. In: Ignatov, D., et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-52920-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-52920-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52919-6

  • Online ISBN: 978-3-319-52920-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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