  Meta-learning approaches have shown great success in vision and language
domains. However, few studies discuss the practice of meta-learning for
large-scale industrial applications. Although e-commerce companies have spent
many efforts on learning representations to provide a better user experience,
we argue that such efforts cannot be stopped at this step. In addition to
learning a strong profile, the challenging question about how to effectively
transfer the learned representation is raised simultaneously. This paper
introduces the contributions that we made to address these challenges from
three aspects. 1) Meta-learning model: In the context of representation
learning with e-commerce user behavior data, we propose a meta-learning
framework called the Meta-Profile Network, which extends the ideas of matching
network and relation network for knowledge transfer and fast adaptation; 2)
Encoding strategy: To keep high fidelity of large-scale long-term sequential
behavior data, we propose a time-heatmap encoding strategy that allows the
model to encode data effectively; 3) Deep network architecture: A multi-modal
model combined with multi-task learning architecture is utilized to address the
cross-domain knowledge learning and insufficient label problems. Moreover, we
argue that an industrial model should not only have good performance in terms
of accuracy, but also have better robustness and uncertainty performance under
extreme conditions. We evaluate the performance of our model with extensive
control experiments in various extreme scenarios, i.e. out-of-distribution
detection, data insufficiency and class imbalance scenarios. The Meta-Profile
Network shows significant improvement in the model performance when compared to
baseline models.
