GPS: Factorized group preference-based similarity models for sparse sequential recommendation

YeongWook Yang, Danial Hooshyar, Heuiseok Lim

Abstract: Highlights•One of the key tasks for recommender systems is the prediction of personalized sequential behavior.•There are two primary means of modeling sequential patterns and long-term user preferences: Markov chains and matrix factorization, respectively.•This proposed approach, called GPS (a factorized Group Preference-based Similarity model), furthermore leverages the idea of group preference along with user preference in order to introduce a greater array of interactions between users.