The Euclidean shortest path problem (ESPP) is a well studied problem with many practical applications.
We achieve the above edge by formulating a multi-objective custom loss function that does not need ground truth labels to quantify the quality of a given data-space partition, making it entirely unsupervised.
In this paper, we propose a deep learning-based framework, called DeepAltTrip, that learns to recommend top-k alternative itineraries for given source and destination POIs.
Indoor location-based services (LBS), such as POI search and routing, are often built on top of typical indoor spatial queries.
Databases Data Structures and Algorithms
Traditional approaches rely on the analysis of text data related to users to accomplish this task.
Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen that compares the quality (as perceived by the users) of the alternative routes generated by four of the most popular existing approaches including the routes provided by Google Maps.