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Structural Learning of Diverse Ranking
Compared with traditional methods, the advantages of our approach lie in that: (1) Directly optimizing DCEM as the loss function is more fundamental for the task; (2) Our framework does not rely on explicit diversity information such as subtopics, thus is more adaptive to real application; (3) The representation of diversity as the feature-based scoring function is more flexible to incorporate rich diversity-based features into the learning framework.
Topic-focused Dynamic Information Filtering in Social Media
For a hot topic, new opinions or ideas will be continuously produced in the form of online data stream.
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