PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks
This addresses a domain-specific problem for navigation service providers by improving path ranking quality, but it is incremental as it builds on existing multi-task learning and embedding techniques.
The paper tackles the problem of ranking multiple paths in spatial networks for navigation services by proposing PathRank, a multi-task learning framework that uses historical trajectories to assign ranking scores, with empirical studies showing it is effective and practical.
Modern navigation services often provide multiple paths connecting the same source and destination for users to select. Hence, ranking such paths becomes increasingly important, which directly affects the service quality. We present PathRank, a data-driven framework for ranking paths based on historical trajectories using multi-task learning. If a trajectory used path P from source s to destination d, PathRank considers this as an evidence that P is preferred over all other paths from s to d. Thus, a path that is similar to P should have a larger ranking score than a path that is dissimilar to P. Based on this intuition, PathRank models path ranking as a regression problem, where each path is associated with a ranking score. To enable PathRank, we first propose an effective method to generate a compact set of training data: for each trajectory, we generate a small set of diversified paths. Next, we propose a multi-task learning framework to solve the regression problem. In particular, a spatial network embedding is proposed to embed each vertex to a feature vector by considering both road network topology and spatial properties, such as distances and travel times. Since a path is represented by a sequence of vertices, which is now a sequence of feature vectors after embedding, recurrent neural network is applied to model the sequence. The objective function is designed to consider errors on both ranking scores and spatial properties, making the framework a multi-task learning framework. Empirical studies on a substantial trajectory data set offer insight into the designed properties of the proposed framework and indicating that it is effective and practical.