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Probabilistic matrix factorization (PMF) is a well-known model of recommendersystems. With the development of image recognition technology, some PMFrecommender systems that combine images have emerged. Some of these systems usethe image shape features of the recommended products to achieve better resultscompared to those of the traditional PMF. However, in the existing methods, noPMF recommender system can combine the image features of products previouslypurchased by customers and of recommended products. Thus, this study proposes anovel probabilistic model that integrates double convolutional neural networks(CNNs) into PMF. For apparel goods, two trained CNNs from the image shapefeatures of users and items are combined, and the latent variables of users anditems are optimized based on the vectorized features of CNNs and ratings.Extensive experiments show that our model predicts outcome more accurately thando other recommender models.

Apparel Recommender System based on Bilateral image shape features