Click Model

Click models aim to understand and predict user interaction with ranked lists, such as search engine results or recommender system outputs, by modeling the probability of clicks based on item relevance and position bias. Current research focuses on developing more sophisticated models, including neural networks (like recurrent and transformer architectures), causal models to address confounding variables, and reinforcement learning approaches for online learning to rank. These advancements improve the accuracy of click prediction, enabling better personalization, more effective advertising attribution, and fairer ranking algorithms across various applications like e-commerce and web search.