  Route recommendation is significant in navigation service. Two major
challenges for route recommendation are route representation and user
representation. Different from items that can be identified by unique IDs in
traditional recommendation, routes are combinations of links (i.e., a road
segment and its following action like turning left) and the number of
combinations could be close to infinite. Besides, the representation of a route
changes under different scenarios. These facts result in severe sparsity of
routes, which increases the difficulty of route representation. Moreover, link
attribute deficiencies and errors affect preciseness of route representation.
Because of the sparsity of routes, the interaction data between users and
routes are also sparse. This makes it not easy to acquire user representation
from historical user-item interactions as traditional recommendations do. To
address these issues, we propose a novel learning framework R4. In R4, we
design a sparse & dense network to obtain representations of routes. The sparse
unit learns link ID embeddings and aggregates them to represent a route, which
captures implicit route characteristics and subsequently alleviates problems
caused by link attribute deficiencies and errors. The dense unit extracts
implicit local features of routes from link attributes. For user
representation, we utilize a series of historical navigation to extract user
preference. R4 achieves remarkable performance in both offline and online
experiments.
