Learning Effective Representations from Sparse Mutlimodal Data on Content Curation Social Networks Content curation social networks (CCSNs), which provide users a platform to share their interests by multimedia information, are the most rapidly growing social networks in recent years. Since largescale multimodal data have been generated by CCSN users, learning multimodal representations for contents have become the key to the progress of many applications such as user interest analysis and recommender system for curation networks. Learning representations for CCSNs faces a vital challenge: the sparsity of multimodal data. It is difficult for most existing approaches to learn effective representations for multimodal CCSNs because they didnu0027t provide a solution on how to model sparse and noisy multimodal data. In this paper, we propose a 2step approach to learn accurate multimodal representations from sparse multimodal data. First, we propose a novel BoardImageWord (BIW) graph to model the multimodal data. Benefited from the unique boardimage relation on CCSNs, embeddings of images and texts which endow semantic relations are learned from the network topology of the BIW graph. As the second step, a deep vision model with modified loss function are trained by minimizing the distance between the visual features of contents and their corresponding semantic relation embeddings to learn representations which incorporate visual information and graphbased semantic relations. Experiments on the dataset from Huaban.com demonstrate that under the circumstance of sparser text modality, our method significantly outperformed multimodal DBN, DBM and unimodal representation learning methods on pin classification and board recommendation tasks.

Sparse MultiGraph Ranking towards Social Image Retrieval xe2x80xa0 Graph ranking is one of popular and successful technique for information retrieval. However, conventional graph ranking has two shortcomings when deployed for social image search. First, as social tags are noisy and incomplete, using that, the initial ranked list of images is inaccurate and impacts the following visual reranking. Another tough issue is how to conduct querysensitive image reranking when multiple visual feature sets are available. In this work, we propose a sparse multigraph ranking framework, in which multiple graphs built on different visual features are integrated to simultaneously encode the image ranking. In particular, a sparse constraint is imposed on the fusion of different features, hoping to select a compact yet informative combination of features for different queries. To deal with the highly noisy issue inherent in social tags, a tag refinement solution along with word embedding is utilized to derive the more accurate initial ranking list, which services as the supervision signal for the proposed graph ranking framework. Extensive experimental analyses and evaluations on NUSWIDE dataset demonstrate the proposed method can achieve stateoftheart performance.