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

  • Tony Lee
  • Seon-Ho Kim
  • Marco Balduini
  • Daniele Dell’Aglio
  • Irene Celino
  • Yi Huang
  • Volker Tresp
  • Emanuele Della Valle
Conference paper

DOI: 10.1007/978-3-319-06826-8_20

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8388)
Cite this paper as:
Lee T. et al. (2014) Location-Based Mobile Recommendations by Hybrid Reasoning on Social Media Streams. In: Kim W., Ding Y., Kim HG. (eds) Semantic Technology. JIST 2013. Lecture Notes in Computer Science, vol 8388. Springer, Cham

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.

Keywords

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

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tony Lee
    • 1
  • Seon-Ho Kim
    • 1
  • Marco Balduini
    • 2
  • Daniele Dell’Aglio
    • 3
  • Irene Celino
    • 3
  • Yi Huang
    • 4
  • Volker Tresp
    • 4
  • Emanuele Della Valle
    • 2
  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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