  Personalized Route Recommendation (PRR) aims to generate user-specific route
suggestions in response to users' route queries. Early studies cast the PRR
task as a pathfinding problem on graphs, and adopt adapted search algorithms by
integrating heuristic strategies. Although these methods are effective to some
extent, they require setting the cost functions with heuristics. In addition,
it is difficult to utilize useful context information in the search procedure.
To address these issues, we propose using neural networks to automatically
learn the cost functions of a classic heuristic algorithm, namely A* algorithm,
for the PRR task. Our model consists of two components. First, we employ
attention-based Recurrent Neural Networks (RNN) to model the cost from the
source to the candidate location by incorporating useful context information.
Instead of learning a single cost value, the RNN component is able to learn a
time-varying vectorized representation for the moving state of a user. Second,
we propose to use a value network for estimating the cost from a candidate
location to the destination. For capturing structural characteristics, the
value network is built on top of improved graph attention networks by
incorporating the moving state of a user and other context information. The two
components are integrated in a principled way for deriving a more accurate cost
of a candidate location. Extensive experiment results on three real-world
datasets have shown the effectiveness and robustness of the proposed model.
