PAVAL: A location-aware virtual personal assistant for retrieving geolocated points of interest and location-based services

A scoping review on the use, processing and fusion of geographic data in virtual assistants

“…Tourism‐related scenarios clearly stand out (10/27, 37%). A large proportion of these cases utilize geoinformation resources for user positioning (Ali, Le, Kim, Hwang, & Hwang, 2019; Chen, Wu, & Wang, 2019; Garrido, Barrachina, Martinez, & Seron, 2017; Grazioso, Cera, Di Maro, Origlia, & Cutugno, 2018; Klopfenstein, Delpriori, Paolini, & Bogliolo, 2018a; Massai, Nesi, & Pantaleo, 2019) to identify points of interest (POIs) near the user (i.e. traveler, tourist, etc.).…”

Geo-Spatial Market Segmentation & Characterization Exploiting User Generated Text Through Transformers & Density-Based Clustering

“…Korakakis et al [3] proposed a system to improve POI recommendations and tourism routes by exploiting social media information. Similarly, [24] worked on a locationaware personal assistant for retrieving POI and services. And Fernández-Gavilanes et al [25] implemented a methodology to differentiate users by language and location.…”

Context-Based Cultural Visits

“…Recommender systems provide custom suggestions based on filtering techniques on many different domains, topics and items. These systems recommend items that are predicted to better match user preferences, thereby reducing the user's cognitive and information overload [19,20].…”

“…Therefore, in recent years, traditional recommender systems started to take into account many dimensions as relevant aspects of the users' information needs. For this reason, context-aware recommender systems started to gain even more attention within the research community [19].…”

“…Nowadays, there exists an increasing amount of georeferenced data and users are typically interested only in local or nearby contexts and resources. The addition of the spatial dimension and, specifically, local geographic information in recommendation systems provides more effective and useful suggestions, which has led to a new application field within CARS called Location-Aware Recommender Systems [19,21].…”