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A location history-aware recommender system for smart retail environments

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Abstract

Recommender systems (RSs) represent integral parts of e-commerce platforms for almost two decades now. The recent emergence of mobile context-aware RSs (CARS) contributed in improving the relevance of recommendations derived by “traditional” RSs through adapting them to the situational user context. This article presents the design and implementation aspects of a collaborative filtering-based mobile CARS, which has been integrated in a smart retailing platform that enables location-based search for retail products and services. In addition to user location, the introduced CARS considers several context parameters like time, season, demographic data, consumer behavior, and location history of the user in order to derive more meaningful product recommendations. Our RS has undergone field trials as well as formal laboratory evaluation tests demonstrating higher accuracy and relevance of recommendations compared with two baseline approaches.

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Notes

  1. http://www.smartbuy.tech/

  2. Rand index or Rand measure, in statistics, is a measure of the similarity between two data clustering.

  3. https://play.google.com/store/apps/details?id=com.smartbuyshopping.app

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Funding

This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T1EDK-01572). V. Kasapakis, G. Pantziou and C. Zaroliagis have been partially supported by the EU H2020 Programme under grant agreement no. 687960 (SMARTBUY).

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Authors and Affiliations

  1. Department of Cultural Technology & Communication, University of the Aegean, Mytilene, Greece

    Thomas Chatzidimitris & Vlasios Kasapakis

  2. Department of Product & Systems Design Engineering, University of the Aegean, Syros, Greece

    Damianos Gavalas

  3. CTI, Patras, Greece

    Vlasios Kasapakis, Charalampos Konstantopoulos, Grammati Pantziou & Christos Zaroliagis

  4. Department of Informatics, University of Piraeus, Piraeus, Greece

    Charalampos Konstantopoulos & Damianos Kypriadis

  5. Department of Informatics & Computer Engineering, University of West Attica, Athens, Greece

    Grammati Pantziou

  6. Department of Computer Engineering & Informatics, University of Patras, Patras, Greece

    Christos Zaroliagis

Authors
  1. Thomas Chatzidimitris
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  2. Damianos Gavalas
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  3. Vlasios Kasapakis
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  4. Charalampos Konstantopoulos
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  5. Damianos Kypriadis
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  6. Grammati Pantziou
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  7. Christos Zaroliagis
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Corresponding author

Correspondence to Damianos Gavalas.

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Chatzidimitris, T., Gavalas, D., Kasapakis, V. et al. A location history-aware recommender system for smart retail environments. Pers Ubiquit Comput 24, 683–694 (2020). https://doi.org/10.1007/s00779-020-01374-7

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  • DOI: https://doi.org/10.1007/s00779-020-01374-7

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