A Content- and Context-Aware Click Model Based on Dynamic Graph Neural Networks

Click modeling constitutes a pivotal area of study within information retrieval, as it provides insights into user search behavior and enables the extraction of valuable implicit relevance feedback from large-scale click logs. However, existing click models often rely on content-agnostic IDs to represent queries and documents. Additionally, they employ context-independent assumptions, such as the examination hypothesis, in modeling click probabilities. As a result, contemporary click models often fall short of capturing the influence of the diverse, multi-modal content found on modern Search Engine Result Pages (SERPs) and the intricate contextual interactions among heterogeneous search results. To address this issue, we propose a novel Dynamic Graph Neural Click Model (DGCM). The proposed model incorporates rich content and context information by jointly representing them as nodes in a dynamic graph and further leverages a dynamic graph attention network to predict users’ clicks at different timesteps. To demonstrate the effectiveness of the DGCM model, we conducted extensive experiments on two large-scale datasets with both content and click information: the public Sogou-SRR dataset and a proprietary dataset collected on the WeChat platform. The experimental results indicate that by capitalizing on the content and context information, DGCM outperforms existing click models in the click prediction and relevance estimation tasks.