Recommendation of Short-Term Activity Sequences During Distributed Events

Diana Nurbakova 1 Léa Laporte 1 Sylvie Calabretto 1 Jérôme Gensel 2
1 DRIM - Distribution, Recherche d'Information et Mobilité
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 LIG Laboratoire d'Informatique de Grenoble - STEAMER
LIG - Laboratoire d'Informatique de Grenoble
Abstract : The amount of social events has increased significantly and location-based services have become an integral part of our life. This makes the recommendation of activity sequences an important emerging application. Recently, the notion of a distributed event (e.g. festival or cruise) that gathers multiple competitive activities has appeared in the literature. An attendee of such events is overwhelmed with numerous possible activities and faces the problem of activity selection with the goal to maximise satisfaction of experience. This selection is subject to various uncertainties. In this paper, we formulate the problem of recommendation of activity sequences as a combination of personalised event recommendation and scheduling problem. We present a new integrated framework to solve it and two computation strategies to analyse the categorical, temporal and textual users' interests. We mine the users' historical traces to extract their behavioural patterns and use them in the construction of the itinerary. The evaluation of our approach on a dataset built over a cruise program shows an average improvement of 9.7% over the state-of-the-art.
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Diana Nurbakova, Léa Laporte, Sylvie Calabretto, Jérôme Gensel. Recommendation of Short-Term Activity Sequences During Distributed Events. International Conference on Computational Science (ICCS 2017), Jun 2017, Zürich, Switzerland. pp.2069-2078, ⟨10.1016/j.procs.2017.05.154⟩. ⟨hal-01568796⟩

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