Fashion Sensitive Clothing Recommendation Using Hierarchical Collocation Model

Abstract

Automatic clothing recommendation grows dramatically due to the booming of apparel e-commerce. In this paper, we propose a novel clothing recommendation approach which is sensitive to the fashion trend. The proposed approach incorporates the expert knowledge into multiple dimensional information including purchase behaviors, image contents and product descriptions so as to provide recommendation of clothing in line with the forefront of fashion. Meanwhile, to meet with human visual aesthetics and user's collocation experience, we propose the integration of the convolutional neural network and the hierarchical collocation model (HCM) into our framework. The former is to extract effective visual features and attribute descriptors from the clothing items, while the latter embeds them into the concept of style topics which interpret the collocation pattern from a higher level of semantic knowledge. Such a data driven recommendation approach is able to learn clothing collocation metric from multi-dimensional clothing information. Experimental results show that our HCM method achieves better performance than other state-of-the-art baselines. Besides, it also ensures the fashion sensitivity of the recommended outfits.