  Estimating click-through rate (CTR) accurately has an essential impact on
improving user experience and revenue in sponsored search. For CTR prediction
model, it is necessary to make out user real-time search intention. Most of the
current work is to mine their intentions based on user real-time behaviors.
However, it is difficult to capture the intention when user behaviors are
sparse, causing the behavior sparsity problem. Moreover, it is difficult for
user to jump out of their specific historical behaviors for possible interest
exploration, namely weak generalization problem. We propose a new approach
Graph Intention Network (GIN) based on co-occurrence commodity graph to mine
user intention. By adopting multi-layered graph diffusion, GIN enriches user
behaviors to solve the behavior sparsity problem. By introducing co-occurrence
relationship of commodities to explore the potential preferences, the weak
generalization problem is also alleviated. To the best of our knowledge, the
GIN method is the first to introduce graph learning for user intention mining
in CTR prediction and propose end-to-end joint training of graph learning and
CTR prediction tasks in sponsored search. At present, GIN has achieved
excellent offline results on the real-world data of the e-commerce platform
outperforming existing deep learning models, and has been running stable tests
online and achieved significant CTR improvements.
