Modeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach
This work addresses the challenge of time-dependent recommendation for users by improving accuracy through better modeling of interest dynamics, though it is incremental as it extends existing methods.
The paper tackles the problem of modeling changing user preferences in recommender systems by proposing a hidden semi-Markov model to capture heterogeneous durations of user interest states, and experiments on three real-world datasets show it significantly outperforms state-of-the-art time-dependent and static benchmark methods.
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, leading to studies on time-dependent recommender systems. However, most existing approaches that 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 capturing the different durations of user stays in a (latent) interest state, which can better model the heterogeneity of user interests and focuses. We derive an expectation maximization algorithm to estimate the parameters of the framework and predict users' actions. Experiments on three real-world datasets show that our model significantly outperforms the state-of-the-art time-dependent and static benchmark methods. Further analyses of the experiment results indicate that the performance improvement is related to the heterogeneity of state durations and the drift of user interests in the dataset.