BACO compresses recommender embedding tables by over 75% using balanced co-clustering of users and items that maximizes intra-cluster connectivity while enforcing size balance, with at most 1.85% recall drop and up to 346X speedup over baselines.
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HINA introduces heterogeneous interaction networks to model and analyze multi-entity learning processes at individual, dyadic, and group levels, demonstrated via a case study on AI-mediated collaborative learning.
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Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems
BACO compresses recommender embedding tables by over 75% using balanced co-clustering of users and items that maximizes intra-cluster connectivity while enforcing size balance, with at most 1.85% recall drop and up to 346X speedup over baselines.
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Heterogeneous Interaction Network Analysis (HINA): A New Learning Analytics Approach for Modelling, Analyzing, and Visualizing Complex Interactions in Learning Processes
HINA introduces heterogeneous interaction networks to model and analyze multi-entity learning processes at individual, dyadic, and group levels, demonstrated via a case study on AI-mediated collaborative learning.