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A Generic Framework for Cross Domain Recommendation

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Intelligent Information and Database Systems: Recent Developments (ACIIDS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 830))

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Abstract

Cross-domain recommender systems research focuses on recommendation improvement in the target domain based on knowledge gathered from the source domain. Recently, a number of review studies were published, however, they did not provide their reader with a step-by-step guide to conduct cross-domain recommender systems research. In this paper, we present a generic cross domain recommender systems framework which identified different phases and components required to execute a cross-domain recommendation research. The proposed framework is based on analysis of primary studies collected from recently published high impact review studies. It was found that all of the collected primary studies included identified phases, however, each component had multiple options (approaches) which can be applied based on scenarios. The proposed framework can be used for analysis and comparison of existing primary studies, and also explains why cross domain recommender systems approaches are evaluated the same as conventional recommender system approaches. Finally, conclusion and future direction are presented.

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Authors and Affiliations

  1. Universiti Teknologi Malaysia, 81310, Skudai, Malaysia

    Muhammad Murad Khan & Roliana Ibrahim

Authors
  1. Muhammad Murad Khan
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  2. Roliana Ibrahim
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Corresponding author

Correspondence to Muhammad Murad Khan .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Maciej Huk

  2. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

  3. Department of Management, Gdańsk University of Technology, Gdańsk, Poland

    Edward Szczerbicki

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Khan, M.M., Ibrahim, R. (2020). A Generic Framework for Cross Domain Recommendation. In: Huk, M., Maleszka, M., Szczerbicki, E. (eds) Intelligent Information and Database Systems: Recent Developments. ACIIDS 2019. Studies in Computational Intelligence, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-030-14132-5_26

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