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Modern internet technologies make people easily access millions of songs. However it also forms a huge barrier between customers and those songs people truely want, due to the difficulty to explore this large collections. In this paper, we propose a novel music playlist prediction algorithm to facilitate this process for users. This method captures the moods expressed by songs in playlist context...
In this paper, a novel graph-based transductive classification approach, called linear neighborhood propagation, is proposed. The basic idea is to predict the label of a data point according to its neighbors in a linear way. This method can be cast into a second-order intrinsic Gaussian Markov random field framework. Its result corresponds to a solution to an approximate inhomogeneous biharmonic equation...
Graph-based inference plays an important role in many mining and learning tasks. Among all the solvers for this problem, belief propagation (BP) provides a general and efficient way to derive approximate solutions. However, for large scale graphs the computational cost of BP is still demanding. In this paper, we propose a multilevel algorithm to accelerate belief propagation on Markov Random Fields...
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