  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.
