The field of recommender systems and online advertising is moving towards more accurate and reliable methods for predicting user behavior and improving model performance. One notable direction is the use of generative models to enhance the precision of click-through rate predictions, as well as the development of innovative datasets and evaluation metrics to better understand user attention and purchasing behavior. Another key area of research is the identification of offline metrics that predict online impact, enabling more informed decision-making in real-world applications. Additionally, there is a growing focus on making online advertisements more accessible and trustworthy for autonomous web agents, which pose a threat to traditional display advertising. Noteworthy papers include:

• A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages, which contributes a comprehensive dataset for studying user attention and purchasing behavior.
• Correcting the LogQ Correction, which proposes a refined correction formula for improving model quality in large-scale retrieval tasks.