A comparative study of matrix factorization and random walk with restart in recommender systems
A comparative study of matrix factorization and random walk with restart in recommender systems
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DOI：
10.1109/bigdata.2017.8257991
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发表时间：
2017-08
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影响因子：
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通讯作者：
Haekyu Park;Jinhong Jung;U. Kang
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作者：
Haekyu Park;Jinhong Jung;U. Kang
在矩阵分解和重启随机游走（RWR）之间，哪种方法更适合推荐系统？哪种方法更好地处理显式或隐式反馈数据？其他信息是否有助于推荐？推荐系统在亚马逊和Netflix等许多电子商务服务中发挥着重要作用，可以向用户推荐新商品。在各种推荐策略中，协同过滤通过使用用户的评分模式显示出良好的性能。矩阵分解和重启随机游走是最具代表性的协同过滤方法。然而，目前还不清楚哪种方法提供更好的推荐性能，尽管它们的广泛实用性。在本文中，我们提供了一个比较研究的矩阵分解和RWR推荐系统。我们根据推荐中的不同任务，精确地表达了这两种方法的对应关系。特别是，我们新设计了一种使用全局偏差项的RWR方法，该方法对应于使用偏差的矩阵分解方法。我们详细介绍了这两种方法在推荐质量的各个方面，如这些方法如何处理冷启动问题，通常发生在协同过滤。我们在真实世界的数据集上进行了广泛的实验，以评估每种方法在各种措施方面的性能。我们观察到，矩阵分解执行更好的显式反馈评级，而RWR是更好的隐式的。我们还观察到，利用项目的全球流行度是有利的性能和边信息产生积极的协同作用，显式反馈，但与隐式的负面影响。
Between matrix factorization or Random Walk with Restart (RWR), which method works better for recommender systems? Which method handles explicit or implicit feedback data better? Does additional information help recommendation? Recommender systems play an important role in many ecommerce services such as Amazon and Netflix to recommend new items to a user. Among various recommendation strategies, collaborative filtering has shown good performance by using rating patterns of users. Matrix factorization and random walk with restart are the most representative collaborative filtering methods. However, it is still unclear which method provides better recommendation performance despite their extensive utility. In this paper, we provide a comparative study of matrix factorization and RWR in recommender systems. We exactly formulate each correspondence of the two methods according to various tasks in recommendation. Especially, we newly devise an RWR method using global bias term which corresponds to a matrix factorization method using biases. We describe details of the two methods in various aspects of recommendation quality such as how those methods handle cold-start problem which typically happens in collaborative filtering. We extensively perform experiments over real-world datasets to evaluate the performance of each method in terms of various measures. We observe that matrix factorization performs better with explicit feedback ratings while RWR is better with implicit ones. We also observe that exploiting global popularities of items is advantageous in the performance and that side information produces positive synergy with explicit feedback but gives negative effects with implicit one.