Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

13 Jan 2021 Chen Ma Liheng Ma Yingxue Zhang Ruiming Tang Xue Liu Mark Coates

Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them. Although matrix factorization and deep learning based methods have proved effective in user preference modeling, they violate the triangle inequality and fail to capture fine-grained preference information... (read more)

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