  Recommender systems help users find relevant items of interest based on the
past preferences of those users. In many domains, however, the tastes and
preferences of users change over time due to a variety of factors and
recommender systems should 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 based on Hidden Markov Models (HMM) which takes into
account the dynamics of user preferences. We propose a HMM-based approach to
change point detection 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 in two ways. In one approach change points
are used to create a sequence-aware non-negative matrix factorization model to
generate recommendations that are aligned with the current tastes of user. In
the second approach the HMM is used directly to generate recommendations taking
into account the identified change points. These models are evaluated in terms
of accuracy of change point detection and also the effectiveness of
recommendations using a real music streaming dataset.
