document: Factorizing personalized Markov chains for next-basket recommendation Recommender systems are an important component of many websites . Two of the most popular approaches are based on matrix factorization ( MF ) and Markov chains ( MC ) . MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences . On the other hand , MC methods model sequential behavior by learning a transition graph over items that is used to predict the next action based on the recent actions of a user . In this paper , we present a method bringing both approaches together . Our method is based on personalized transition graphs over underlying Markov chains . That means for each user an own transition matrix is learned - thus in total the method uses a transition cube . As the observations for estimating the transitions are usually very limited , our method factorizes the transition cube with a pairwise interaction model which is a special case of the Tucker Decomposition . We show that our factorized personalized MC ( FPMC ) model subsumes both a common Markov chain and the normal matrix factorization model . For learning the model parameters , we introduce an adaption of the Bayesian Personalized Ranking ( BPR ) framework for sequential basket data . Empirically , we show that our FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization .
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