  Tagging has been recognized as a successful practice to boost relevance
matching for information retrieval (IR), especially when items lack rich
textual descriptions. A lot of research has been done for either multi-label
text categorization or image annotation. However, there is a lack of published
work that targets at item tagging specifically for IR. Directly applying a
traditional multi-label classification model for item tagging is sub-optimal,
due to the ignorance of unique characteristics in IR. In this work, we propose
to formulate item tagging as a link prediction problem between item nodes and
tag nodes. To enrich the representation of items, we leverage the query logs
available in IR tasks, and construct a query-item-tag tripartite graph. This
formulation results in a TagGNN model that utilizes heterogeneous graph neural
networks with multiple types of nodes and edges. Different from previous
research, we also optimize both full tag prediction and partial tag completion
cases in a unified framework via a primary-dual loss mechanism. Experimental
results on both open and industrial datasets show that our TagGNN approach
outperforms the state-of-the-art multi-label classification approaches.
