Personalized Fashion Recommendation With Discrete Content-Based Tensor Factorization
Zhi Lu, Yang Hu, Cong Yu, Yunchao Jiang, Yan Chen, Bing Zeng
Abstract
Fashion outfit recommendation has attracted lots of attention recently. The problem becomes even more interesting and challenging when considering users’ personalized fashion preferences. Although existing works have successfully improved the recommendation accuracy, the efficiency issue of computation and storage is still under-investigated and often ignored. In this paper, we propose a discrete content-based tensor factorization model that maps items and user to binary codes for efficient fashion recommendation. We introduce a probabilistic perspective for learning to hash, where the binary codes are sampled from a set of underlying Bernoulli variables. To demonstrate the effectiveness of our model, we collect a large-scale outfit dataset together with user label information from a fashion-focused social website. Extensive experiments on our dataset show that the proposed model outperforms other state-of-the-art methods.