  To better exploit search logs and model users' behavior patterns, numerous
click models are proposed to extract users' implicit interaction feedback. Most
traditional click models are based on the probabilistic graphical model (PGM)
framework, which requires manually designed dependencies and may oversimplify
user behaviors. Recently, methods based on neural networks are proposed to
improve the prediction accuracy of user behaviors by enhancing the expressive
ability and allowing flexible dependencies. However, they still suffer from the
data sparsity and cold-start problems. In this paper, we propose a novel
graph-enhanced click model (GraphCM) for web search. Firstly, we regard each
query or document as a vertex, and propose novel homogeneous graph construction
methods for queries and documents respectively, to fully exploit both
intra-session and inter-session information for the sparsity and cold-start
problems. Secondly, following the examination hypothesis, we separately model
the attractiveness estimator and examination predictor to output the
attractiveness scores and examination probabilities, where graph neural
networks and neighbor interaction techniques are applied to extract the
auxiliary information encoded in the pre-constructed homogeneous graphs.
Finally, we apply combination functions to integrate examination probabilities
and attractiveness scores into click predictions. Extensive experiments
conducted on three real-world session datasets show that GraphCM not only
outperforms the state-of-art models, but also achieves superior performance in
addressing the data sparsity and cold-start problems.
