Deep Neural Network Incorporating CNN and MF for Item-Based Fashion Recommendation

In fashion electronic commerce services, two item-based recommendation approaches, image similarity-based and click likelihood-based, are used to improve the revenue of a website. To improve accuracy, in this paper, we propose a hybrid model, a deep neural network (DNN) that predicts click probability of a target fashion item by incorporating both image similarity and click likelihood. To create an image similarity feature, we acquire a latent image feature through a CNN-based classification of fashion color, type and pattern. To create a click likelihood feature, we calculate matrix factorization (MF) and use decomposed item features as latent click log feature. To solve a cold-start problem (recommendation of new items), we complement the latent log features of new items with those of existing ones. An offline evaluation shows that the accuracy of proposed model (both log and image) improved by 14% compared with matrix factorization (log only) and 56% the image-only model. Moreover, the complement of latent log features changes the new item ratio to six times.