  Trip itinerary recommendation finds an ordered sequence of Points-of-Interest
(POIs) from a large number of candidate POIs in a city. 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.
These alternative itineraries would be not only popular given the historical
routes adopted by past users but also dissimilar (or diverse) to each other.
The DeepAltTrip consists of two major components: (i) Itinerary Net (ITRNet)
which estimates the likelihood of POIs on an itinerary by using graph
autoencoders and two (forward and backward) LSTMs; and (ii) a route generation
procedure to generate k diverse itineraries passing through relevant POIs
obtained using ITRNet. For the route generation step, we propose a novel
sampling algorithm that can seamlessly handle a wide variety of user-defined
constraints. To the best of our knowledge, this is the first work that learns
from historical trips to provide a set of alternative itineraries to the users.
Extensive experiments conducted on eight popular real-world datasets show the
effectiveness and efficacy of our approach over state-of-the-art methods.
