Abstract
Itinerary recommenders provide tourists with personalized routes connecting several Points of Interest (POIs). Therefore transit times and users' preferences have to be considered to generate optimal plans. Nevertheless users might appreciate routes being customised to their liking, e.g. based on further contextual factors the system does not know of. Additionally new knowledge on the go, e.g. an ...
Abstract
Itinerary recommenders provide tourists with personalized routes connecting several Points of Interest (POIs). Therefore transit times and users' preferences have to be considered to generate optimal plans. Nevertheless users might appreciate routes being customised to their liking, e.g. based on further contextual factors the system does not know of. Additionally new knowledge on the go, e.g. an unexpectedly overcrowded POI, might make it necessary to adapt plans.
In this paper we present a system that is able to recommend itineraries and allows users to customize them via manual editing. We investigate, via 2 large-scale naturalistic studies (n=1235 and n=2649), how these editing operations were performed. To this end logs of user interactions with the system were collected. The results of the analysis of these data reveal some surprising usage patterns and point to how such systems can better serve users' needs. Our main conclusion is that itinerary recommendations can benefit from incorporating additional knowledge about users' preferences derived from how users modifiy their route. Moreover, assistance on the go can be improved by suggesting better route alternatives in case of unexpected incidents by imitating the modifications users would manually perform.
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