  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.
