RecWalk:用于Top-N推荐的几乎不耦合随机行走

A. Nikolakopoulos, G. Karypis
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引用次数: 61

摘要

随机漫步可以提供一个强大的工具,用于收集基于项目的模型中捕获的丰富交互网络,以进行top-n推荐。它们可以利用项目之间的间接关系,减轻稀疏性的影响,确保更广泛的项目空间覆盖,并增加推荐列表的多样性。然而,它们的潜力受到行走迅速集中到图的中心节点的趋势的阻碍,从而极大地限制了可用于个性化推荐的k步分布的范围。在这项工作中,我们介绍了RecWalk;一种新颖的基于随机行走的方法,利用几乎不耦合的马尔可夫链的频谱特性来证明解除了这一限制,并延长了用户过去偏好对行走后续步骤的影响——允许行走者更有效地探索底层网络。在现实世界数据集上进行的一组全面的实验验证了所提出方法的理论预测属性,并表明它们与top-n推荐准确性的显着提高直接相关。他们还强调了RecWalk在为提高基于项目的模型的性能提供框架方面的潜力。RecWalk实现了最先进的顶级推荐质量,优于几种竞争方法,包括最近提出的依赖深度神经网络的方法。
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RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation
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.
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