Collaborative Fashion Recommendation: A Functional Tensor Factorization Approach

With the rapid expansion of online shopping for fashion products, effective fashion recommendation has become an increasingly important problem. In this work, we study the problem of personalized outfit recommendation, i.e. automatically suggesting outfits to users that fit their personal fashion preferences. Unlike existing recommendation systems that usually recommend individual items, we suggest sets of items, which interact with each other, to users. We propose a functional tensor factorization method to model the interactions between user and fashion items. To effectively utilize the multi-modal features of the fashion items, we use a gradient boosting based method to learn nonlinear functions to map the feature vectors from the feature space into some low dimensional latent space. The effectiveness of the proposed algorithm is validated through extensive experiments on real world user data from a popular fashion-focused social network.