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Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones), which may cause unpredictable deviation of resultant cluster labels from genuine ones, thereby leading to severe information loss and performance...
As the most widely used recommendation algorithm, collaborative filtering (CF) has been studied for many years due to its simplicity and effectiveness. The two main categories of CF have their own shortcomings. Memory-based CF can't generate accurate results when faced with data sparsity; and model-based CF always loses the information between users or items. To alleviate this problem, we propose...
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