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Recommendations in location-based social networks: a survey / Jie Bao in Geoinformatica, vol 19 n° 3 (July - September 2015)  

Public [article]  inGeoinformatica > vol 19 n° 3 (July - September 2015) . - pp 525 - 565 Titre : Recommendations in location-based social networks: a survey Type de document : Article/Communication Auteurs : Jie Bao, Auteur ; David Wilkie, Auteur ; Mohamed Mokbe, Auteur Année de publication : 2015 Article en page(s) : pp 525 - 565 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse comparative [Termes IGN] analyse de données [Termes IGN] géobalise [Termes IGN] performance [Termes IGN] positionnement automatique [Termes IGN] réseau social géodépendant [Termes IGN] source de données [Termes IGN] système de recommandation [Termes IGN] utilisateur Résumé : (auteur) Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users’ preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users’ travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research theme. Numéro de notice : A2015-497 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-014-0220-8 Date de publication en ligne : 06/02/2015 En ligne : https://doi.org/10.1007/s10707-014-0220-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77309 [article]