Personalized difusions for top-n recommendation

Athanasios N. Nikolakopoulos, Dimitris Berberidis, George Karypis, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationRecSys 2019 - 13th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)9781450362436
StatePublished - Sep 10 2019
Event13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark
Duration: Sep 16 2019Sep 20 2019

Publication series

NameRecSys 2019 - 13th ACM Conference on Recommender Systems


Conference13th ACM Conference on Recommender Systems, RecSys 2019

Bibliographical note

Funding Information:
This work was supported in part by NSF (1447788, 1704074, 1757916, 1834251), Army Research Ofce (W911NF1810344), Intel Corp, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.

Publisher Copyright:
© 2019 Copyright held by the owner/author(s).


  • Item Models
  • Random Walks
  • Top-N Recommendation


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