Unsupervised Path Representation Learning with Curriculum Negative Sampling
This addresses the need for task-agnostic path representations in transportation systems, reducing reliance on labeled data, but it is incremental as it builds on unsupervised and contrastive learning techniques.
The paper tackles the problem of learning generic path representations for transportation applications without labeled data, proposing an unsupervised framework called Path InfoMax (PIM) that uses curriculum negative sampling and mutual information maximization, and it significantly outperforms other unsupervised methods in tasks like ranking score and travel time estimation.
Path representations are critical in a variety of transportation applications, such as estimating path ranking in path recommendation systems and estimating path travel time in navigation systems. Existing studies often learn task-specific path representations in a supervised manner, which require a large amount of labeled training data and generalize poorly to other tasks. We propose an unsupervised learning framework Path InfoMax (PIM) to learn generic path representations that work for different downstream tasks. We first propose a curriculum negative sampling method, for each input path, to generate a small amount of negative paths, by following the principles of curriculum learning. Next, \emph{PIM} employs mutual information maximization to learn path representations from both a global and a local view. In the global view, PIM distinguishes the representations of the input paths from those of the negative paths. In the local view, \emph{PIM} distinguishes the input path representations from the representations of the nodes that appear only in the negative paths. This enables the learned path representations to encode both global and local information at different scales. Extensive experiments on two downstream tasks, ranking score estimation and travel time estimation, using two road network datasets suggest that PIM significantly outperforms other unsupervised methods and is also able to be used as a pre-training method to enhance supervised path representation learning.