Le Wu

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Collaborative Filtering(CF) is a popular way to build recommender systems and has been successfully employed in many applications. Generally, two kinds of approaches to CF, the <i>local</i> neighborhood methods and the <i>global</i> matrix factorization models, have been widely studied. Though some previous researches target on combining the complementary(More)
The problem of multi-label classification has attracted great interests in the last decade. Multi-label classification refers to the problems where an example that is represented by a single instance can be assigned to more than one category. Until now, most of the researches on multi-label classification have focused on supervised settings whose assumption(More)
P2P lending is an online platform to make borrowing and investment transactions. A central question on these platforms is how to align the right products with the right investors, thus helping investors to make better decisions. Along this line, tremendous efforts have been devoted to modeling the credits of products and borrowers from an economic(More)
Collaborative filtering (CF) models offer users personalized recommendations by measuring the <i>relevance</i> between the active user and each individual candidate item. Following this idea, user-based collaborative filtering (UCF) usually selects the local popular items from the like-minded neighbor users. However, these traditional relevance-based models(More)
An adaptive filter for video sequence super resolution reconstruction is proposed based on weighted least square and total variation regularization. We thoroughly study the motion compensation matrix and weighted matrix, and deduce the recursive filter formula. Experimental results demonstrate the proposed method to be adaptive and robust.
With the booming popularity of online social networks like Twitter and Weibo, online user footprints are accumulating rapidly on the social web. Simultaneously, the question of how to leverage the large-scale user-generated social media data for personal credit scoring comes into the sight of both researchers and practitioners. It has also become a topic of(More)