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Collaborative Filtering for Producing Recommendations in the Retail Sector

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Information Systems (EMCIS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 299))

Abstract

Recommender Systems exploit implicit or explicit user feedback, to create recommendations and provide a personalized user experience. In the case of explicit feedback datasets, the system directly collects the user opinion. On the other hand, to compile implicit feedback datasets the system works passively in the background, tracking different sorts of user behavior, such as browsing activity, watching habits or purchase history. In this work, we focus on implicit feedback recommendation systems. We analyze their unique characteristics and identify their differences to the much more extensively researched explicit feedback systems.

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Author information

Authors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Dimitrios Poulopoulos & Dimosthenis Kyriazis

Authors
  1. Dimitrios Poulopoulos
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  2. Dimosthenis Kyriazis
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Corresponding author

Correspondence to Dimitrios Poulopoulos .

Editor information

Editors and Affiliations

  1. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Marinos Themistocleous

  2. Department of Management and Technology, Bocconi University, Milan, Italy

    Vincenzo Morabito

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Poulopoulos, D., Kyriazis, D. (2017). Collaborative Filtering for Producing Recommendations in the Retail Sector. In: Themistocleous, M., Morabito, V. (eds) Information Systems. EMCIS 2017. Lecture Notes in Business Information Processing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-65930-5_52

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  • DOI: https://doi.org/10.1007/978-3-319-65930-5_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65929-9

  • Online ISBN: 978-3-319-65930-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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