Personalized difusions for top-n recommendation

This paper introduces PerDif; a novel framework for learning personalized difusions over item-to-item graphs for top-n recommendation. PerDif learns the teleportation probabilities of a time-inhomogeneous random walk with restarts capturing a user-specifc underlying item exploration process. Such an approach can lead to signifcant improvements in recommendation accuracy, while also providing useful information about the users in the system. Per-user ftting can be performed in parallel and very efciently even in large-scale settings. A comprehensive set of experiments on real-world datasets demonstrate the scalability as well as the qualitative merits of the proposed framework. PerDif achieves high recommendation accuracy, outperforming state-of-the-art competing approaches-including several recently proposed methods relying on deep neural networks.