  Trip recommender system, which targets at recommending a trip consisting of
several ordered Points of Interest (POIs), has long been treated as an
important application for many location-based services. Currently, most prior
arts generate trips following pre-defined objectives based on constraint
programming, which may fail to reflect the complex latent patterns hidden in
the human mobility data. And most of these methods are usually difficult to
respond in real time when the number of POIs is large. To that end, we propose
an Adversarial Neural Trip Recommendation (ANT) framework to tackle the above
challenges. First of all, we devise a novel attention-based encoder-decoder
trip generator that can learn the correlations among POIs and generate
well-designed trips under given constraints. Another novelty of ANT relies on
an adversarial learning strategy integrating with reinforcement learning to
guide the trip generator to produce high-quality trips. For this purpose, we
introduce a discriminator, which distinguishes the generated trips from
real-life trips taken by users, to provide reward signals to optimize the
generator. Moreover, we devise a novel pre-train schema based on learning from
demonstration, which speeds up the convergence to achieve a
sufficient-and-efficient training process. Extensive experiments on four
real-world datasets validate the effectiveness and efficiency of our proposed
ANT framework, which demonstrates that ANT could remarkably outperform the
state-of-the-art baselines with short response time.
