Jingwen Bian

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Microblogging services have emerged as an essential way to strengthen the communications among individuals. One of the most important features of microblog over traditional social networks is the extensive proliferation in information diffusion. As the outbreak of information diffusion often brings in valuable opportunities or devastating effects, it will(More)
Good representations of data do help in many machine learning tasks such as recommendation. It is often a great challenge for traditional recommender systems to learn representative features of both users and images in large social networks, in particular, social curation networks, which are characterized as the extremely sparse links between users and(More)
Query suggestion is an effective solution to help users deliver their search intent. While many query suggestion approaches have been proposed for test-based image retrieval with query-by-keywords, query suggestion for content-based image retrieval (CBIR) with query-by-example (QBE) has been seldom studied. QBE usually suffers from the <i>"intention(More)
Microblogging services have revolutionized the way people exchange information. Confronted with the ever-increasing numbers of social events and the corresponding microblogs with multimedia contents, it is desirable to provide visualized summaries to help users to quickly grasp the essence of these social events for better understanding. While existing(More)
This work presents a new interactive Content Based Image Retrieval (CBIR) scheme, termed Attribute Feedback (AF). Unlike traditional relevance feedback purely founded on low-level visual features, the Attribute Feedback system shapes users' information needs more precisely and quickly by collecting feedbacks on intermediate level semantic attributes. At(More)
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