Ranking Companion integrates six complementary item-selection methods with model-driven active learning in a visual analytics interface to support iterative personalized ranking creation via known-item judgments.
Towards User-Centered Active Learning Algorithms,
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Ranking Companion: A Visual Analytics Approach to Item-Based Ranking with Hybrid Item Selection
Ranking Companion integrates six complementary item-selection methods with model-driven active learning in a visual analytics interface to support iterative personalized ranking creation via known-item judgments.