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ThinkMind // UBICOMM 2017, The Eleventh International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies // View article ubicomm_2017_6_20_10043


Enhancing the Affective Sensitivity of Location Based Services Using Situation-Person-Dependent Semantic Similarity

Authors:
Antonios Karatzoglou
Michael Beigl

Keywords: LBS; Location Prediction, Ontologies, Dynamic Semantic Similarity, Personality Traits, Emotional State, Personalization, Cognitive Frames

Abstract:
Location prediction plays a steadily growing role in Location Based Services (LBS), as these try to be more proactive and improve in this way the quality of service provided. Although recent location prediction systems go beyond just location data and build upon a wide range of models that describe semantic locations and personal preferences, none of them considers locations from the view of the user. Moreover, none takes into account the variance in people's way of perceiving and understanding concepts (locations in our case) depending on the situation. Minsky referred to this as (cognitive) emph{frames}. This paper posits that a dynamic semantic-similarity-based clustering of locations can be used for mining such location-specific frames, e.g. the varying meanings that people give to locations over time depending on the situation, their personality and their emotional state. The resulting situation-person-specific frames can in turn be used to enhance the location prediction.

Pages: 95 to 100

Copyright: Copyright (c) IARIA, 2017

Publication date: November 12, 2017

Published in: conference

ISSN: 2308-4278

ISBN: 978-1-61208-598-2

Location: Barcelona, Spain

Dates: from November 12, 2017 to November 16, 2017

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