ORIGINAL ARTICLE A location history-aware recommender system for smart retail environments Thomas Chatzidimitris 1 & Damianos Gavalas 2 & Vlasios Kasapakis 1,3 & Charalampos Konstantopoulos 3,4 & Damianos Kypriadis 4 & Grammati Pantziou 3,5 & Christos Zaroliagis 3,6 Received: 17 October 2019 /Accepted: 23 January 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Recommender systems (RSs) represent integral parts of e-commerce platforms for almost two decades now. The recent emer- gence 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. Keywords Recommender system . Collaborative filtering . E-commerce . M-commerce . Retailer shop . Shopping mall . Smart retailing . Location-based search . Context awareness . Location history 1 Introduction Recommendation systems (RSs) are information filtering sys- tems aiming at predicting the “rating” (i.e., the preference) that a user would give to an information item (e.g., music file, book, or any other product) or social element (e.g., people or groups) she has not yet considered [8]. RSs recommend those items predicted to better match user preferences, thereby re- ducing the user ’s cognitive and information load. Recommendations are made either implicitly (e.g., through ranking a list of information items or displaying a “those you bought this product, also bought those” bar) or explicitly (when the user requests a recommendation). The applications of RSs include, but are not limited to, the areas of e-com- merce, e-learning, e-library, e-government, e-tourism, and e- business services [17]. Τhe rapid development of mobile computing technolo- gies generated a new thread of research within the field of * Damianos Gavalas dgavalas@aegean.gr Thomas Chatzidimitris tchatz@aegean.gr Vlasios Kasapakis v.kasapakis@aegean.gr Charalampos Konstantopoulos konstant@unipi.gr Damianos Kypriadis dkypriad@unipi.gr Grammati Pantziou pantziou@uniwa.gr Christos Zaroliagis zaro@ceid.upatras.gr 1 Department of Cultural Technology & Communication, University of the Aegean, Mytilene, Greece 2 Department of Product & Systems Design Engineering, University of the Aegean, Syros, Greece 3 CTI, Patras, Greece 4 Department of Informatics, University of Piraeus, Piraeus, Greece 5 Department of Informatics & Computer Engineering, University of West Attica, Athens, Greece 6 Department of Computer Engineering & Informatics, University of Patras, Patras, Greece Personal and Ubiquitous Computing https://doi.org/10.1007/s00779-020-01374-7