  Learning feature interactions is the key to success for the large-scale CTR
prediction in Ads ranking and recommender systems. In industry, deep neural
network-based models are widely adopted for modeling such problems. Researchers
proposed various neural network architectures for searching and modeling the
feature interactions in an end-to-end fashion. However, most methods only learn
static feature interactions and have not fully leveraged deep CTR models'
representation capacity. In this paper, we propose a new model: DynInt. By
extending Polynomial-Interaction-Network (PIN), which learns higher-order
interactions recursively to be dynamic and data-dependent, DynInt further
derived two modes for modeling dynamic higher-order interactions: dynamic
activation and dynamic parameter. In dynamic activation mode, we adaptively
adjust the strength of learned interactions by instance-aware activation gating
networks. In dynamic parameter mode, we re-parameterize the parameters by
different formulations and dynamically generate the parameters by
instance-aware parameter generation networks. Through instance-aware gating
mechanism and dynamic parameter generation, we enable the PIN to model dynamic
interaction for potential industry applications. We implement the proposed
model and evaluate the model performance on real-world datasets. Extensive
experiment results demonstrate the efficiency and effectiveness of DynInt over
state-of-the-art models.
