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Discovering semantic coherent topics from the large amount of user-generated content (UGC) in social media would facilitate many downstream applications of intelligent computing. Topic models, as one of the most powerful algorithms, have been widely used to discover the latent semantic patterns in text collections. However, one key weakness of topic models is that they need documents with certain...
In the field of recommender systems, the Beer & Nappies is a famous story, which reveals the latent relationships between different categories of items. Though matrix factorization (MF) has demonstrated its great effectiveness in most previous work, it neglects the co-occurrences of items selected by individuals. In most MF-based models, the latent preferences of users (or the latent categories...
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