Personalized Search in Smart Indoor Environments: Combining a Formal Location Model, User Preferences and Semantic Similarity

Wenyi Xu 1 Christophe Marsala 1
1 LFI - Learning, Fuzzy and Intelligent systems
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In the web of things (WOT) paradigm, it is possible for users to have access to a big amount of connected objects to fulfil their requests. However, finding the right object is a difficult task as the search should take into account not only the functionalities of the objects but also their physical localisation and their distance from the user. In this paper, a new approach to build a WOT search engine is introduced. A new semantic similarity is proposed to compare objects in ontology. To answer a user's request, the proposed model recommends objects according to both their geo-localisation and capabilities. Moreover, the search of objects takes into account the user's profile and expectations. The solution we proposed relies on fuzzy rule engines and a formal location model that characterise the search space in which relevant connected objects are selected.
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Conference papers
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https://hal.sorbonne-universite.fr/hal-01303037
Contributor : Christophe Marsala <>
Submitted on : Friday, April 15, 2016 - 4:47:04 PM
Last modification on : Thursday, March 21, 2019 - 1:06:06 PM

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  • HAL Id : hal-01303037, version 1

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Wenyi Xu, Christophe Marsala. Personalized Search in Smart Indoor Environments: Combining a Formal Location Model, User Preferences and Semantic Similarity. IEEE World Congress on Computational Intelligence (WCCI'2016), Jul 2016, Vancouver, Canada. pp.1768-1775. ⟨hal-01303037⟩

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