Apparel Goods Recommender System Based on Image Shape Features Extracted by a CNN
Ryosuke Saga, Yufeng Duan · 2018
Recommender system is an information-filtering tool used in solving the problem that the user's preference in information overload. In recent years, some algorithms have been combined with some side information (i.e., item description documents, user reviews, and social networks), and rating prediction accuracy has been significantly improved. However, for fashionable goods, such as apparel and shoes that are important for designing, the contextual information of items is insufficient, and their image shape feature should be considered. Currently, no such recommender system is available to use this feature of image shape. This study proposes a novel probabilistic model using the image shape feature that integrates a convolutional neural network into the probabilistic matrix factorization. The experiment conducted on two real-world datasets corroborates that our model outperforms the other recommendation models.