Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models
This addresses the need for more adaptive recommender systems in online settings, though it is incremental as it builds on existing sequence-aware methods.
The authors tackled the problem of adapting recommender systems to changing user preferences by proposing a Hidden Markov Model (HMM) to detect change-points in user interaction sequences, which improved recommendation performance compared to state-of-the-art models.
In a variety of online settings involving interaction with end-users it is critical for the systems to adapt to changes in user preferences. User preferences on items tend to change over time due to a variety of factors such as change in context, the task being performed, or other short-term or long-term external factors. Recommender systems need to be able to capture these dynamics in user preferences in order to remain tuned to the most current interests of users. In this work we present a recommendation framework which takes into account the dynamics of user preferences. We propose an approach based on Hidden Markov Models (HMM) to identify change-points in the sequence of user interactions which reflect significant changes in preference according to the sequential behavior of all the users in the data. The proposed framework leverages the identified change points to generate recommendations using a sequence-aware non-negative matrix factorization model. We empirically demonstrate the effectiveness of the HMM-based change detection method as compared to standard baseline methods. Additionally, we evaluate the performance of the proposed recommendation method and show that it compares favorably to state-of-the-art sequence-aware recommendation models.