  Click-through rate (CTR) prediction plays an important role in online
advertising and recommendation systems, which aims at estimating the
probability of a user clicking on a specific item. Feature interaction modeling
and user interest modeling methods are two popular domains in CTR prediction,
and they have been studied extensively in recent years. However, these methods
still suffer from two limitations. First, traditional methods regard item
attributes as ID features, while neglecting structure information and relation
dependencies among attributes. Second, when mining user interests from
user-item interactions, current models ignore user intents and item intents for
different attributes, which lacks interpretability. Based on this observation,
in this paper, we propose a novel approach Hierarchical Intention Embedding
Network (HIEN), which considers dependencies of attributes based on bottom-up
tree aggregation in the constructed attribute graph. HIEN also captures user
intents for different item attributes as well as item intents based on our
proposed hierarchical attention mechanism. Extensive experiments on both public
and production datasets show that the proposed model significantly outperforms
the state-of-the-art methods. In addition, HIEN can be applied as an input
module to state-of-the-art CTR prediction methods, bringing further performance
lift for these existing models that might already be intensively used in real
systems.
