Advertisement
Published in:
2013 | OriginalPaper | Chapter
10. The Big Picture: Toward a Synthesis of RL and Adaptive Tensor Factorization
Authors : Alexander Paprotny, Michael Thess
Published in: Realtime Data Mining
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.