Random walks can provide a powerful tool for harvesting the rich network of interactions captured within item-based models for top-n recommendation. They can exploit indirect relations between the items, mitigate the effects of sparsity, ensure wider itemspace coverage, as well as increase the diversity of recommendation lists. Their potential however, is hindered by the tendency of the walks to rapidly concentrate towards the central nodes of the graph, thereby significantly restricting the range of K-step distributions that can be exploited for personalized recommendations. In this work we introduce RecWalk; a novel random walk-based method that leverages the spectral properties of nearly uncoupled Markov chains to provably lift this limitation and prolong the influence of users' past preferences on the successive steps of the walk-allowing the walker to explore the underlying network more fruitfully. A comprehensive set of experiments on real-world datasets verify the theoretically predicted properties of the proposed approach and indicate that they are directly linked to significant improvements in top-n recommendation accuracy. They also highlight RecWalk's potential in providing a framework for boosting the performance of item-based models. RecWalk achieves state-of-the-art top-n recommendation quality outperforming several competing approaches, including recently proposed methods that rely on deep neural networks.
|Original language||English (US)|
|Title of host publication||WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||9|
|State||Published - Jan 30 2019|
|Event||12th ACM International Conference on Web Search and Data Mining, WSDM 2019 - Melbourne, Australia|
Duration: Feb 11 2019 → Feb 15 2019
|Name||WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining|
|Conference||12th ACM International Conference on Web Search and Data Mining, WSDM 2019|
|Period||2/11/19 → 2/15/19|
Bibliographical noteFunding Information:
This work was supported in part by NSF (1447788, 1704074, 1757916, 1834251), Army Research Office (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.
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