A Graph-Enhanced Click Model for Web Search
Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Shuai Li, Ruiming, Tang, Xiuqiang He, Jianye Hao, Yong Yu

TL;DR
This paper introduces GraphCM, a novel graph-enhanced click model that leverages homogeneous graph construction and neural networks to improve web search user behavior prediction, especially addressing data sparsity and cold-start issues.
Contribution
The paper proposes a new graph-based framework for click modeling that exploits intra- and inter-session information using graph neural networks, enhancing prediction accuracy and robustness.
Findings
GraphCM outperforms state-of-the-art models on real-world datasets.
It effectively mitigates data sparsity and cold-start problems.
The model achieves higher click prediction accuracy.
Abstract
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…
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Taxonomy
TopicsRecommender Systems and Techniques · Advanced Graph Neural Networks · Information Retrieval and Search Behavior
