ISBN: 978-981-09-5471-0 DOI: 10.18178/wcse.2015.04.061
Ranking via Hypergraph Learning Integration of Textual Content and Visual Content
Abstract— 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.
Index Terms— Ranking; Hypergraph Learning; Product Search; Visual Search.
Kaiman Zeng, Nansong Wu, Arman Sargolzaei, Kang K. Yen
Department of Electrical and Computer Engineering Florida International University, US
Cite: Kaiman Zeng, Nansong Wu, Arman Sargolzaei, Kang K. Yen, "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 (WCSE 2015-IPCE), pp. 367-372, Moscow, Russia, April 15-17, 2015.