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.
How to build a machine that can continuously learn from observations in its life and make accurate inference/prediction? This is one of the central questions in Artificial Intelligence. Many challenges are present, such as the difficulty of learning from infinitely many observations (data), the dynamic nature of the environments, noisy and sparse data, the intractability of posterior inference, etc. This tutorial will discuss how the Bayesian approach provides a natural and efficient answer. We will start from the basic of Bayesian models, and then the variational Bayes method for inference. Next, we will discuss how to learn a Bayesian model from an infinite sequence of data. Some challenges such as catastrophic forgetting phenomenon, concept drifts, and overfitting will be discussed.