基于近似非耦合随机游走的项目协同过滤算法
Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks
摘要：
基于项目的模型是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获这些模型中捕获的丰富的交互网络。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，以及增加推荐列表的多样性。然而，它们的潜力可能会受到步行快速集中到图的中心节点的趋势的阻碍，从而大大限制了可以用于个性化推荐的K步分布的范围。在这项工作中，我们介绍RecWalk;一种新的基于随机行走的方法，利用几乎解耦的马尔可夫链的谱特性来证明解除这种限制，并延长用户过去偏好对行走的连续步骤的影响-允许步行者更有效地探索底层网络。在真实世界数据集上进行的一组全面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推荐准确性的显着改善直接相关。他们还强调了RecWalk在提供一个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top-n推荐质量，优于几种竞争方法，包括最近提出的依赖于深度神经网络的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
