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

•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.