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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Rand index or Rand measure, in statistics, is a measure of the similarity between two data clustering.
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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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Chatzidimitris, T., Gavalas, D., Kasapakis, V. et al. A location history-aware recommender system for smart retail environments. Pers Ubiquit Comput (2020). https://doi.org/10.1007/s00779-020-01374-7
- Recommender system
- Collaborative filtering
- Retailer shop
- Shopping mall
- Smart retailing
- Location-based search
- Context awareness
- Location history