Modeling the sequential behaviors of online users in recommender systems

Analyzing sequential user behaviors plays an important role to build an effective recommender system and it has been paid a great deal of attention by researchers. Previous work exploits two types of sequential behaviors of users: Item sequence (each user interacts with items in order) and sequential interactions on an item (e.g. clicking an item, then adding it to cart, finally purchasing it). While a vast number of studies focus on modeling item sequence, a few works exploit sequential interactions on an item in recent years. However, there is no work that focuses on both of them. In our work, we propose a novel model which directly models both the types to capture user behaviors completely. Our model can combine multiple types of behaviors as a sequence of actions, moreover, it can model users' preferences through time with the sequence items which they have interacted in the past. The intensively experimental results show that our model significantly outperforms the effective baselines which are designed to learn from either item sequence or sequential user interactions.