2016/03/31 by Ruining He, Charles Packer, He, Ruining +3 · 1 citation
Computer Science · #Artificial intelligence #Compatibility (geochemistry) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Database #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Information retrieval #Laptop #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Natural language processing #Recommender Systems and Techniques #Recommender system #Scalability #cs.CV #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.1603.09473
11 pages, 5 figures
openalex publication_date 2016/03/31 · arxiv created 2016/09/29 · arxiv updated 2016/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Identifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compatible. In domains like clothing recommendation, this task is particularly challenging since a successful system should be capable of handling a large corpus of items, a huge amount of relationships among them, as well as the high-dimensional and semantically complicated features involved. Furthermore, the human notion of "compatibility" to capture goes beyond mere similarity: For two items to be compatible---whether jeans and a t-shirt, or a laptop and a charger---they should be similar in some ways, but systematically different in others. In this paper we propose a novel method, Monomer, to learn complicated and heterogeneous relationships between items in product recommendation settings. Recently, scalable methods have been developed that address this task by learning similarity metrics on top of the content of the products involved. Here our method relaxes the metricity assumption inherent in previous work and models multiple localized notions of 'relatedness,' so as to uncover ways in which related items should be systematically similar, and systematically different. Quantitatively, we show that our system achieves state-of-the-art performance on large-scale compatibility prediction tasks, especially in cases where there is substantial heterogeneity between related items. Qualitatively, we demonstrate that richer notions of compatibility can be learned that go beyond similarity, and that our model can make effective recommendations of heterogeneous content.