Learning to Style-Aware Bayesian Personalized Ranking for Visual Recommendation

Abstract

Recently, product images have been gaining the attention of recommender system researchers in the field of visual recommendation. This is because the visual appearance of products has a significant impact on consumers' decisions. Extensive studies have been done to integrate the features extracted by convolutional neural networks directly into recommendations. This improves the performance of recommender systems. Style features, an important type of features, are rarely considered. Style features play a vital role in the visual recommendation as a user's decision depends largely on whether the product fits his/her style. However, the representation of the conventional image features fails in capturing the styles of a product. To bridge this gap, we propose introducing style feature modeling, which is highly relevant with user preference, into the visual recommendation model. Furthermore, we propose incorporating the style features into collaborative learning to create awareness pertaining to the preferences of users. The experiments conducted on two public implicit feedback datasets demonstrate the effectiveness of our approach for the visual recommendation.