A Hidden Semi-Markov Approach for Timedependent Recommendation

Recommender systems are widely used for suggesting books, education materials, and products to users by exploring their behaviors . In reality, users’ preferences often change over time, which leads to the studies on time-dependent recommender systems. However, most existing approaches to deal with time information remain primitive. In this paper, we extend existing methods and propose a hidden semi-Markov model to track the change of users’ interests. Particularly, this model allows for users to stay in different (latent) interest states for different time periods , which is beneficial to model the heterogeneous length of users’ interest and focuses. We derive an EM algorithm to estimate the parameter of the framework , and predict users’ actions . Experiments on a real-world datase t show that our model significantly outperforms the state-of-the-art benchmark methods. Further analyses show that the performance depends on the allowed heterogeneity of latent states and the existence of user interest heterogeneity in the dataset.