Personalized diffusions for top-n recommendation

This paper introduces PerDif; a novel framework for learning personalized diffusions 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-specific underlying item exploration process. Such an approach can lead to significant improvements in recommendation accuracy, while also providing useful information about the users in the system. Per-user fitting can be performed in parallel and very efficiently 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.