Learning Driver Preferences of POIs using a Semantic Web Knowledge System

Presented at: 9th Extended Semantic Web Conference (ESWC2012)

by Rahul Parundekar, Kentaro Oguchi

In this paper, we present the architecture and implementation of a Semantic Web Knowledge System that is employed to learn driver preferences for Points of Interest (POIs) using a content based approach. Initially, implicit & explicit feedback is collected from drivers about the places that they like. Data about these places is then retrieved from web sources and a POI preference model is built using machine learning algorithms. At a future time, when the driver searches for places that he/she might want to go to, his/her learnt model is used to personalize the result. The data that is used to learn preferences is represented as Linked Data with the help of a POI ontology, and retrieved from multiple POI search services by `lifting' it into RDF. This structured data is then combined with driver context and fed into a machine learning algorithm that produces a statistical model of the driver's preferences. This data and model is hosted in the cloud and is accessible via intelligent services and an access control mechanism to a client device like an in-vehicle navigation system. We describe the design and implementation of such a system that is currently in-use to study how a driver's preferences can be modeled by the vehicle platform and utilized for POI recommendations.

Keywords: Architecture, Linked Data, Preference Modeling, Semantic Web Knowledge Base

Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2012/paper/in-use-industrial/255

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