Ranking via Hypergraph Learning Integration of Textual Content and Visual Content
2015 The 5th International Workshop on Computer Science and Engineering-Information Processing and Control Engineering · 2015
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.