Ranking has been widely researched in information retrieval and machine learning. Yet it is still a challenging problem, especially in visual product search. In this paper, we propose a novel hypergraph learning based ranking model by mining the correlations among products' textual and visual features. We formally define a unified hypergraph based ranking framework for product search. Each product image is regarded as a vertex in a hypergraph. The hypergraph captures various high-order relations among different products' information, including visual content, product categorization labels, and product descriptions. We conducted experiments on the proposed ranking algorithm on a data set collected from various e-commerce websites. The results of our comparison demonstrate the effectiveness of our proposed algorithm.
