RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation

Athanasios Nikolakopoulos, George Karypis

Abstract:

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 lever-
ages 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 com-
prehensive 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 recom-
mendation quality outperforming several competing approaches,
including recently proposed methods that rely on deep neural net-
works.