The top- N recommendation problem has been studied extensively. Item-based collaborative filtering recommendation algorithms show promising results for the problem. They predict a user’s preferences by estimating similarities between a target and user-rated items. Top- N recommendation remains a challenging task in scenarios where there is a lack of preference history for new items. Feature-based Similarity Models (FSMs) address this particular problem by extending item-based collaborative filtering by estimating similarity functions of item features. The quality of the estimated similarity function determines the accuracy of the recommendation. However, existing FSMs only estimate global similarity functions; i.e., they estimate using preference information across all users. Moreover, the estimated similarity functions are linear ; hence, they may fail to capture the complex structure underlying item features. In this article, we propose to improve FSMs by estimating local similarity functions, where each function is estimated for a subset of like-minded users. To capture global preference patterns, we extend the global similarity function from linear to nonlinear, based on the effectiveness of variational autoencoders. We propose a Bayesian generative model, called the Local Variational Feature-based Similarity Model, to encapsulate local and global similarity functions. We present a variational Expectation Minimization algorithm for efficient approximate inference. Extensive experiments on a large number of real-world datasets demonstrate the effectiveness of our proposed model.

中文翻译：

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用于推荐前 N 个新项目的基于局部变分特征的相似性模型

顶端-ñ推荐问题已被广泛研究。基于项目的协同过滤推荐算法显示了该问题的有希望的结果。他们通过估计目标和用户评分项目之间的相似性来预测用户的偏好。最佳-ñ在缺乏对新项目的偏好历史记录的情况下，推荐仍然是一项具有挑战性的任务。基于特征的相似性模型（FSM）通过估计项目特征的相似性函数来扩展基于项目的协同过滤来解决这个特定问题。估计相似度函数的好坏决定了推荐的准确性。然而，现有的 FSM 仅估计全球的相似函数；即，他们使用所有用户的偏好信息进行估计。此外，估计的相似度函数为线性的; 因此，它们可能无法捕捉到项目特征背后的复杂结构。在本文中，我们建议通过估计局部相似性函数来改进 FSM，其中每个函数都是为志同道合的用户的子集估计的。为了捕捉全局偏好模式，我们基于变分自动编码器的有效性将全局相似度函数从线性扩展到非线性。我们提出了一种贝叶斯生成模型，称为基于局部变分特征的相似度模型，以封装局部和全局相似度函数。我们提出了一种用于有效近似推理的变分期望最小化算法。对大量真实世界数据集的广泛实验证明了我们提出的模型的有效性。