Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks
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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.
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