HOMMIT: A Sequential Recommendation for Modeling Interest-Transferring via High-Order Markov Model

Capturing user interest accurately is a key task for predicting personalized sequential action in recommender systems. Through preliminary investigation, we find that user interest is stable in short term, while changeable in long term. The user interest changes significantly during the interaction with the system, and the duration of a particular interest and the frequency of transition are also personalized. Based on this finding, a recommendation framework called HOMMIT is proposed, which can identify user interests and adapt an improved high-order Markov chain method to model the dynamic transition process of user interests. It can predict the transition trends of user interest and make personalized sequential recommendation. We evaluate and compare multiple implementations of our framework on two large, real-world datasets. The experiments are conducted to prove the high accuracy of our proposed sequential recommendation framework, which verified the importance of considering interest-transferring in recommendations.