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 ...