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—thereby 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.

中文翻译：

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通过几乎不耦合的随机游走提升基于项目的协同过滤

基于项目的模型是用于构建推荐系统的最流行的协同过滤方法之一。随机游走可以提供一个强大的工具来收集这些模型中捕获的丰富的交互网络。他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，以及增加推荐列表的多样性。然而，它们的潜力可能会受到游走快速集中到图的中心节点的趋势的阻碍，从而显着限制了ķ可用于个性化推荐的步骤分布。在这项工作中，我们介绍回顾步行; 一种新颖的基于随机游走的方法，它利用了光谱特性几乎解耦的马尔可夫链可证明地解除这一限制并延长用户过去的偏好对步行连续步骤的影响——从而使步行者能够更有效地探索底层网络。在现实世界数据集上进行的一组综合实验验证了所提出方法的理论预测特性，并表明它们与顶级技术的显着改进直接相关。n推荐准确度。他们还强调了 RecWalk 在提供框架以提高基于项目的模型性能方面的潜力。RecWalk 实现了最先进的n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。