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Heterogeneous Domain Adaptation With Adversarial Neural Representation Learning: Experiments on E-Commerce and
Summary
This study introduces Heterogeneous Adversarial Neural Domain Adaptation (HANDA), a new framework for adapting machine learning models to new domains with limited data. HANDA significantly improves predictive performance in heterogeneous feature spaces, particularly for e-commerce applications.
Area of Science:
- Machine Learning
- Artificial Intelligence
- Computer Science
Background:
- Supervised learning faces challenges adapting models to new domains with scarce data.
- Domain adaptation methods aim to transfer knowledge from source to target domains with different data distributions.
- Heterogeneous Domain Adaptation (HDA) is particularly difficult due to differing feature spaces.
Purpose of the Study:
- To develop a novel framework for effective domain adaptation in heterogeneous feature spaces.
- To enhance the transferability of neural representations in cross-domain learning scenarios.
- To address limitations of existing HDA methods that rely on mathematical optimization and suffer from low transferability.
Main Methods:
- Proposed Heterogeneous Adversarial Neural Domain Adaptation (HANDA) framework.
- Employed a unified neural network architecture for feature and distribution alignment.
- Utilized adversarial kernel learning to achieve domain invariance.
Main Results:
- HANDA demonstrated statistically significant improvements in predictive performance.
- Evaluated against state-of-the-art HDA methods on major e-commerce image and text benchmarks.
- Showcased practical utility in real-world dark web online market analysis.
Conclusions:
- HANDA effectively maximizes transferability in heterogeneous environments.
- The framework represents a significant advancement for domain adaptation in e-commerce.
- HANDA offers a promising solution for leveraging knowledge across domains with differing feature spaces.
