  The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant
labels from an exceptionally large label space. Most of the existing XMC
learners focus on the extraction of semantic features from input query text.
However, conventional XMC studies usually neglect the side information of
instances and labels, which can be of use in many real-world applications such
as recommendation systems and e-commerce product search. We propose Predicted
Instance Neighborhood Aggregation (PINA), a data enhancement method for the
general XMC problem that leverages beneficial side information. Unlike most
existing XMC frameworks that treat labels and input instances as featureless
indicators and independent entries, PINA extracts information from the label
metadata and the correlations among training instances. Extensive experimental
results demonstrate the consistent gain of PINA on various XMC tasks compared
to the state-of-the-art methods: PINA offers a gain in accuracy compared to
standard XR-Transformers on five public benchmark datasets. Moreover, PINA
achieves a $\sim 5\%$ gain in accuracy on the largest dataset
LF-AmazonTitles-1.3M. Our implementation is publicly available.

Machine Learning;Information Retrieval