Joint learning for image-based handbag recommendation

Fashion recommendation helps shoppers to find desirable fashion items, which facilitates online interaction and product promotion. In this paper, we propose a method to recommend handbags to each shopper, based on the handbag images the shopper has clicked. This is performed by Joint learning of attribute Projection and One-class SVM classification (JPO) based on the images of the shopper's preferred handbags. More specifically, for the handbag images clicked by each shopper, we project the original image feature space into an attribute space which is more compact. The projection matrix is learned jointly with a one-class SVM to yield a shopper-specific one-class classifier. The results show that the proposed JPO handbag recommendation performs favorably based on initial subject testing.