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 com...

• Type: Conference
• Year: 2010
• Where: WWW
• Authors: Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme
• Keywords: Internet Technology, Markov Chain, Matrix Factorization, Matrix Factorization Model, WWW 2010