Abstract: This application relates to apparatus and methods for training machine learning models, and applying trained machine learning models to generate item ranking values. In some examples, user session data for multiple users is received. Based on the user session data, user engagement data is generated characterizing engagements of corresponding items for a search query. Further, a number of examines is determined for each of the corresponding items. The user engagement data for each item is normalized based on the corresponding number of examines, and ranking data is generated based on the normalized user engagement data. The ranking data characterizes a ranking of at least a subset of the items. Further, a machine learning model is trained based on the ranking data. In some examples, the trained machine learning model is applied to a query to generate a ranking of items, and the ranking data is transmitted to a web server.