A Context-Aware Click Model for Web Search

To better exploit the search logs, various click models have been proposed to extract implicit relevance feedback from user clicks. Most traditional click models are based on probability graphical models (PGMs) with manually designed dependencies. Recently, some researchers also adopt neural-based methods to improve the accuracy of click prediction. However, most of the existing click models only model user behavior in query level. As the previous iterations within the session may have an impact on the current search round, we can leverage these behavior signals to better model user behaviors. In this paper, we propose a novel neural-based Context-Aware Click Model (CACM) for Web search. CACM consists of a context-aware relevance estimator and an examination predictor. The relevance estimator utilizes session context information, i.e., the query sequence and clickthrough data, as well as the pre-trained embeddings learned from a session-flow graph to estimate the context-aware relevance of each search result. The examination predictor estimates the examination probability of each result. We further investigate several combination functions to integrate the context-aware relevance and examination probability into click prediction. Experiment results on a public Web search dataset show that CACM outperforms existing click models in both relevance estimation and click prediction tasks.