  Learning predictive models in new domains with scarce training data is a
growing challenge in modern supervised learning scenarios. This incentivizes
developing domain adaptation methods that leverage the knowledge in known
domains (source) and adapt to new domains (target) with a different probability
distribution. This becomes more challenging when the source and target domains
are in heterogeneous feature spaces, known as heterogeneous domain adaptation
(HDA). While most HDA methods utilize mathematical optimization to map source
and target data to a common space, they suffer from low transferability. Neural
representations have proven to be more transferable; however, they are mainly
designed for homogeneous environments. Drawing on the theory of domain
adaptation, we propose a novel framework, Heterogeneous Adversarial Neural
Domain Adaptation (HANDA), to effectively maximize the transferability in
heterogeneous environments. HANDA conducts feature and distribution alignment
in a unified neural network architecture and achieves domain invariance through
adversarial kernel learning. Three experiments were conducted to evaluate the
performance against the state-of-the-art HDA methods on major image and text
e-commerce benchmarks. HANDA shows statistically significant improvement in
predictive performance. The practical utility of HANDA was shown in real-world
dark web online markets. HANDA is an important step towards successful domain
adaptation in e-commerce applications.
