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Issue No.02 - Feb. (2013 vol.25)
pp: 448-460
Xin Jin , University of Illinois at Urbana Champaign, Champaign
Jiebo Luo , Eastman Kodak Company, Rochester
Jie Yu , GE Global Research, Niskayuna
Gang Wang , Nanyang Technological University, and Advanced Digital Science Center, Singapore
Dhiraj Joshi , Eastman Kodak Company, Rochester
Jiawei Han , University of Illinois at Urbana Champaign, Champaign
Social multimedia sharing and hosting websites, such as Flickr and Facebook, contain billions of user-submitted images. Popular Internet commerce websites such as are also furnished with tremendous amounts of product-related images. In addition, images in such social networks are also accompanied by annotations, comments, and other information, thus forming heterogeneous image-rich information networks. In this paper, we introduce the concept of (heterogeneous) image-rich information network and the problem of how to perform information retrieval and recommendation in such networks. We propose a fast algorithm heterogeneous minimum order k-SimRank (HMok-SimRank) to compute link-based similarity in weighted heterogeneous information networks. Then, we propose an algorithm Integrated Weighted Similarity Learning (IWSL) to account for both link-based and content-based similarities by considering the network structure and mutually reinforcing link similarity and feature weight learning. Both local and global feature learning methods are designed. Experimental results on Flickr and Amazon data sets show that our approach is significantly better than traditional methods in terms of both relevance and speed. A new product search and recommendation system for e-commerce has been implemented based on our algorithm.
Complexity theory, Semantics, Vectors, Mathematical model, Visualization, Equations, Image edge detection, ranking, Information retrieval, image mining, information network
Xin Jin, Jiebo Luo, Jie Yu, Gang Wang, Dhiraj Joshi, Jiawei Han, "Reinforced Similarity Integration in Image-Rich Information Networks", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 2, pp. 448-460, Feb. 2013, doi:10.1109/TKDE.2011.228
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