Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks

TL;DR

RecWalk is a novel random walk method that enhances item-based collaborative filtering by leveraging nearly uncoupled Markov chains, leading to improved recommendation accuracy and broader network exploration.

Contribution

Introduces RecWalk, a spectral-based random walk technique that mitigates walk concentration issues, significantly boosting item-based collaborative filtering performance.

Findings

RecWalk outperforms existing methods in top-n recommendation accuracy.

The approach effectively explores the item network, reducing sparsity effects.

Experimental results confirm theoretical advantages of nearly uncoupled Markov chains.

Abstract

Item-based models are among the most popular collaborative filtering approaches for building recommender systems. Random walks can provide a powerful tool for harvesting the rich network of interactions captured within these models. 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, can be 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.