Many non-standard learning models, such as semi-supervised learning or transfer learning can be viewed as learning with auxiliary information. We discuss how to formally analyze the benefits of auxiliary information and suggest a formal framework for this. Further, we consider a particular case of learning with auxiliary information, namely regularized least squares regression with an extra inp...

Sequential recommendations have attracted increasing attention from both academia and industry in recent years. They predict a given user’s next choice of items by mainly modeling the sequential relations over sequence interactions with items. However, most existing recommendation algorithms focus on dependencies between item IDs within sequences, while ignoring rich complex embedded auxiliary ...