Study of Context-based Personalized Recommendations for Points of Interest
| Type of publication: | Inproceedings |
| Citation: | |
| Booktitle: | IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology |
| Series: | WI-IAT '21 |
| Year: | 2021 |
| Month: | December |
| Pages: | 482–487 |
| Publisher: | ACM |
| Location: | Melbourne, VIC, Australia |
| Address: | New York, NY, USA |
| ISBN: | 9781450391153 |
| URL: | https://doi.org/10.1145/348662... |
| DOI: | 10.1145/3486622.3493994 |
| Abstract: | 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. |
| Keywords: | contextualized recommendation, geo-fencing, LBA, location-based services, point of interest |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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