The Big Picture: Toward a Synthesis of RL and Adaptive Tensor Factorization

Abstract

We explore the subject of uniting the control-theoretic with the factorization-based approach to recommendation, arguing that tensor factorization may be employed to vanquish combinatorial complexity impediments related to more sophisticated MDP models that take a history of previous states rather than one single state into account. Specifically, we introduce a tensor representation of transition probabilities of Markov-k-processes and devise a Tucker-based approximation architecture that relies crucially on the notion of an aggregation basis described in Chap. 6. As our method requires a partitioning of the set of state transition histories, we are left with the challenge of how to determine a suitable partitioning, for which we propose a genetic algorithm.