Smooth neighborhood recommender systems
Smooth neighborhood recommender systems
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发表时间：
2019-02
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通讯作者：
Ben Dai;Junhui Wang;Xiaotong Shen;A. Qu
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作者：
Ben Dai;Junhui Wang;Xiaotong Shen;A. Qu
推荐系统通过在存在稀疏观测的情况下汇集来自其他用户和/或项目的类似信息来预测用户对大量项目的偏好。一个主要的挑战是如何利用用户-项目特定协变量和网络来描述高维情况下的用户-项目交互，以进行准确的个性化预测。在这篇文章中，我们提出了一个平滑的邻域推荐的潜在因素模型的框架。相似性核被用来从用户-项目特定网络（例如用户的社交网络）上的连续协变量借用邻域信息，其中由离散协变量定义的分组信息也通过网络被整合。因此，用户项目特定信息被内置到推荐器中，以在协作和基于内容的过滤中缺乏观察的情况下对抗“冷启动”问题。此外，我们利用交替最小二乘算法的“分而治之”的版本，以实现可扩展的计算，并建立所提出的方法的渐近结果，表明它实现了上级预测精度。最后，我们说明了所提出的方法大大提高了其竞争对手在模拟的例子和真实的基准数据-Last.fm音乐数据。
Recommender systems predict users’ preferences over a large number of items by pooling similar information from other users and/or items in the presence of sparse observations. One major challenge is how to utilize user-item specific covariates and networks describing user-item interactions in a high-dimensional situation, for accurate personalized prediction. In this article, we propose a smooth neighborhood recommender in the framework of the latent factor models. A similarity kernel is utilized to borrow neighborhood information from continuous covariates over a user-item specific network, such as a user’s social network, where the grouping information defined by discrete covariates is also integrated through the network. Consequently, user-item specific information is built into the recommender to battle the ‘cold-start” issue in the absence of observations in collaborative and content-based filtering. Moreover, we utilize a “divide-and-conquer” version of the alternating least squares algorithm to achieve scalable computation, and establish asymptotic results for the proposed method, demonstrating that it achieves superior prediction accuracy. Finally, we illustrate that the proposed method improves substantially over its competitors in simulated examples and real benchmark data– Last.fm music data.