On the composition and recommendation of multi-feature paths: a comprehensive approach

Trackers have become popular devices these days. They are extensively used to record sports activities (e.g., hiking, skiing), mainly in terms of GPS trajectories, which can be shared on social networking platforms with other users looking for leisure tips. Notably, as the number of available trajectories drastically increased over time, in many cases, it has become challenging, if not impossible, the extensive evaluation of all possible alternatives and the manual selection of the most suitable one. Paths are characterized by multiple features (e.g., dirt, asphalt), and a good representation is needed to satisfy user needs. Moreover, paths can be composed to generate new routes. This calls for a recommender system capable to handle both the multi-feature path representation and the implicit definition of alternatives by composition. This paper suggests a novel approach that features a richer trajectory representation based on a semantic annotation to describe significant path features. Annotations are then used for automatic recommendation of new paths that maximize the presence of characteristics matching the user preferences. Finally, a class of algorithm variants is evaluated using an off-line validation process and compared with a baseline solution to test the underlying assumptions.