In this paper, we introduce BOTTARI: an augmented reality application that offers personalized and location-based recommendations of Point Of Interests based on sentiment analysis with geo-semantic query and reasoning. We present a mobile recommendation platform and application working on semantic technologies (knowledge representation and query for geo-social data, and inductive and deductive stream reasoning), and the lesson learned in deploying BOTTARI in Insadong. We have been collecting and analyzing tweets for three years to rate the few hundreds of restaurants in the district. The results of our study show the commercial feasibility of BOTTARI.
Location-Based Mobile Recommendations by Hybrid Reasoning on Social Media Streams
BALDUINI, MARCO;DELL'AGLIO, DANIELE;DELLA VALLE, EMANUELE
2013-01-01
Abstract
In this paper, we introduce BOTTARI: an augmented reality application that offers personalized and location-based recommendations of Point Of Interests based on sentiment analysis with geo-semantic query and reasoning. We present a mobile recommendation platform and application working on semantic technologies (knowledge representation and query for geo-social data, and inductive and deductive stream reasoning), and the lesson learned in deploying BOTTARI in Insadong. We have been collecting and analyzing tweets for three years to rate the few hundreds of restaurants in the district. The results of our study show the commercial feasibility of BOTTARI.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
