  Distance metric learning has attracted much attention in recent years, where
the goal is to learn a distance metric based on user feedback. Conventional
approaches to metric learning mainly focus on learning the Mahalanobis distance
metric on data attributes. Recent research on metric learning has been extended
to sequential data, where we only have structural information in the sequences,
but no attribute is available. However, real-world applications often involve
attributed sequence data (e.g., clickstreams), where each instance consists of
not only a set of attributes (e.g., user session context) but also a sequence
of categorical items (e.g., user actions). In this paper, we study the problem
of metric learning on attributed sequences. Unlike previous work on metric
learning, we now need to go beyond the Mahalanobis distance metric in the
attribute feature space while also incorporating the structural information in
sequences. We propose a deep learning framework, called MLAS (Metric Learning
on Attributed Sequences), to learn a distance metric that effectively measures
dissimilarities between attributed sequences. Empirical results on real-world
datasets demonstrate that the proposed MLAS framework significantly improves
the performance of metric learning compared to state-of-the-art methods on
attributed sequences.
