Location-Based Mobile Recommendations by Hybrid Reasoning on Social Media Streams

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8388)


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.


Social media analytics Mobile recommendation Stream reasoning Hybrid reasoning Machine learning Semantic Web Ontology 



This work was partially supported by the LarKC project (FP7-215535) and Mobile Cognition and Learning project in Korea.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.SaltluxKangnam-gu SeoulKorea
  2. 2.Dip. Elettronica e InformazionePolitecnico di MilanoMilanoItaly
  3. 3.CEFRIELICT Institute, Politecnico di MilanoMilanoItaly
  4. 4.Siemens AGCorporate TechnologyMünchenGermany

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