Paper Info
Title
RecWalk - Nearly Uncoupled Random Walks for Top-N Recommendation.
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 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.
Year
DOI
Venue
2019
10.1145/3289600.3291016
WSDM
Keywords
Field
DocType
collaborative filtering, item models, random walks, top-n recommendation
Spectral properties,Data mining,Graph,Collaborative filtering,Computer science,Random walk,Markov chain,Exploit,Boosting (machine learning),Deep neural networks
Conference
ISBN
Citations 
PageRank 
978-1-4503-5940-5
9
0.59
References 
Authors
24
2
Name
Order
Citations
PageRank
Athanasios N. Nikolakopoulos1599.02
George Karypis2156911171.82