A location history-aware recommender system for smart retail environments

• Published in: Personal and ubiquitous computing Vol. 24; no. 5; pp. 683 - 694
• Main Authors: Chatzidimitris, Thomas, Gavalas, Damianos, Kasapakis, Vlasios, Konstantopoulos, Charalampos, Kypriadis, Damianos, Pantziou, Grammati, Zaroliagis, Christos
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2020; Springer Nature B.V
• Subjects: Ambient intelligence; Automobiles; Computer Science; Context; Location based services; Mobile Computing; Original Article; Personal Computing; Recommender systems; Retailing; User Interfaces and Human Computer Interaction; Collaborative filtering; Location history; Context awareness; E-commerce; Retailer shop; Location-based search; M-commerce; Smart retailing; Recommender system; Shopping mall
• ISSN: 1617-4909; 1617-4917
• DOI: 10.1007/s00779-020-01374-7

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.