Probabilistic matrix factorization (PMF) is a well-known model of recommender systems. With the development of image recognition technology, some PMF recommender systems that combine images have emerged. Some of these systems use the image shape features of the recommended products to achieve better results compared to those of the traditional PMF. However, in the existing methods, no PMF recommender system can combine the image features of products previously purchased by customers and of recommended products. Thus, this study proposes a novel probabilistic model that integrates double convolutional neural networks (CNNs) into PMF. For apparel goods, two trained CNNs from the image shape features of users and items are combined, and the latent variables of users and items are optimized based on the vectorized features of CNNs and ratings. Extensive experiments show that our model predicts outcome more accurately than do other recommender models.

中文翻译：

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基于双边图像形状特征的服装推荐系统

概率矩阵分解（PMF）是推荐系统的众所周知的模型。随着图像识别技术的发展，出现了一些结合图像的PMF推荐器系统。与传统的PMF相比，其中一些系统使用推荐产品的图像形状功能来获得更好的结果。但是，在现有方法中，没有PMF推荐器系统可以组合先前由客户购买的产品和推荐产品的图像特征。因此，本研究提出了一种将双卷积神经网络（CNN）集成到PMF中的新型概率模型。对于服装商品，结合了来自用户和商品的图像形状特征的两个经过训练的CNN，并基于CNN和等级的矢量化特征优化了用户和商品的潜在变量。