Modular Manifold Ranking for Image Recommendation
Ting Wen Jia, Meng Jian, Lifang Wu, Yonghao He · 2018
With the rapid development of social networking, user requirement suffers more and more from intention gap of user interest and semantic gap of multimedia. It becomes urgent to investigate personalized recommendation. In this paper, we propose modular manifold ranking (MMR) for image recommendation. MMR attempts to construct global image manifold over CNN based features extraction to involve content relations in recommendation. Specifically, manifold modularity is introduced to perform a flexible manifold learning for large scale database in a manner of manifold decomposition. MMR employed manifold ranking to propagate users' interests to the whole image manifold and estimate user-image correlation for image recommendation. The experimental analysis illustrates that the proposed method successfully extends the scalability of manifold ranking and achieves good performance in social image recommendation compared with its competitors.