TY  - CONF
T1  - Study of Context-based Personalized Recommendations for Points of Interest
A1  - Manzo, Gaetano
A1  - Calvaresi, Davide
A1  - Calbimonte, Jean-Paul
A1  - Esteem, Okoro
A1  - Schumacher, Michael
TI  - IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
T3  - WI-IAT '21
Y1  - 2021
SP  - 482–487
PB  - ACM
CY  - Melbourne, VIC, Australia
AD  - New York, NY, USA
SN  - 9781450391153
UR  - https://doi.org/10.1145/3486622.3493994
M2  - doi: 10.1145/3486622.3493994
KW  - contextualized recommendation
KW  - geo-fencing
KW  - LBA
KW  - location-based services
KW  - point of interest
N2  - Location-based services are essential to delivering information for users in the context of travel, leisure, and sports application. Nevertheless, these services are often implemented as recommendations and suggestions that may overwhelm users, or fail to adapt to their goals, behavior, and context. To address these limitations, this paper presents NearMe, an application that provides tailored recommendations of Points of Interest surrounding the user. Beyond existing approaches, NearMe allows the generation of dynamic recommendations from heterogeneous service providers and the definition of regions to which notifications are related. Moreover, it allows to fine-tune notifications, thus preventing over-information and noise. A preliminary study has been conducted involving a heterogeneous group of potential users and service providers that elaborates on their vision, expectations, features desiderata, and possible interfaces.
ER  -