Recommender systems are information filtering tools that seek to match customers with products or services of interest. Most of the prevalent collaborative filtering recommender systems, such as matrix factorization and AutoRec, suffer from the “cold-start” problem, where they fail to provide meaningful recommendations for new users or new items due to informative-missing from the training data. To address this problem, we propose a weighted AutoEncoding model to leverage information from other users or items that share similar characteristics. The proposed method provides an effective strategy for borrowing strength from user or item-specific clustering structure as well as pairwise similarity in the training data, while achieving high computational efficiency and dimension reduction, and preserving nonlinear relationships between user preferences and item features. Simulation studies and applications to three real datasets show advantages in prediction accuracy of the proposed model compared to current state-of-the-art approaches.

中文翻译：

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加权自动编码推荐系统

推荐系统是信息过滤工具，旨在将客户与感兴趣的产品或服务进行匹配。大多数流行的协同过滤推荐系统，例如矩阵分解和 AutoRec，都存在“冷启动”问题，由于训练数据中的信息缺失，它们无法为新用户或新项目提供有意义的推荐。为了解决这个问题，我们提出了一个加权自动编码模型来利用来自其他用户或具有相似特征的项目的信息。所提出的方法提供了一种有效的策略，可以从用户或项目特定的聚类结构以及训练数据中的成对相似性中借用强度，同时实现高计算效率和降维，并保留用户偏好和项目特征之间的非线性关系。与当前最先进的方法相比，对三个真实数据集的仿真研究和应用表明，所提出的模型在预测准确性方面具有优势。