Authors:Christian Samsel;Karl-Heinz Krempels andGerrit Garbereder
Affiliation:RWTH Aachen University, Germany
Keyword(s):Context-aware Computing, Intelligent Transportation Systems, Recommendation Systems, Web Information Systems.
RelatedOntology Subjects/Areas/Topics:Context-Awareness ;Enterprise Information Systems ;Internet Technology ;Mobile Information Systems ;Recommendation Systems ;Software Agents and Internet Computing ;System Integration ;Web Information Systems and Technologies
Abstract:The integration of heterogeneous mobility services increases the number of itinerary choices exponentially.To support travelers with the selection of such an intermodal itinerary this work proposes the use of a recommendationsystem. The developed framework rates intermodal itineraries supplied by an external travelinformation system based on learned personal preferences and user context (e.g. weather). This rating can beused by the client application (e.g. a mobile app) for sorting or a five-star rating. The framework realizes a setof interfaces to extract feature data of the user context and the possible itineraries and applies a combinationof item-based and context-based recommendation algorithms. As evaluation an online questionnaire (n = 101)applying the framework was conducted to assess the feasibility of the approach. The number of participantspreferring the personalized and context-aware itinerary presentation compared to the traditional departuretime-based presentation was significant. Furthermore it could be verified that a mobility self-assessment issuitable as initial training data.(More)
The integration of heterogeneous mobility services increases the number of itinerary choices exponentially.
To support travelers with the selection of such an intermodal itinerary this work proposes the use of a recommendation
system. The developed framework rates intermodal itineraries supplied by an external travel
information system based on learned personal preferences and user context (e.g. weather). This rating can be
used by the client application (e.g. a mobile app) for sorting or a five-star rating. The framework realizes a set
of interfaces to extract feature data of the user context and the possible itineraries and applies a combination
of item-based and context-based recommendation algorithms. As evaluation an online questionnaire (n = 101)
applying the framework was conducted to assess the feasibility of the approach. The number of participants
preferring the personalized and context-aware itinerary presentation compared to the traditional departure
time-based presentation was significant. Furthermore it could be verified that a mobility self-assessment is
suitable as initial training data.