Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
This work addresses the challenge of improving recommendation accuracy for users in personalized systems, though it is incremental as it builds on existing distance-based and probabilistic approaches.
The paper tackled the problem of capturing fine-grained user preferences in recommender systems by developing a distance-based model with probabilistic parameterization and adaptive margins, resulting in a 4-22% improvement in recall@K over state-of-the-art methods on five real-world datasets.
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. To tackle this, we develop a distance-based recommendation model with several novel aspects: (i) each user and item are parameterized by Gaussian distributions to capture the learning uncertainties; (ii) an adaptive margin generation scheme is proposed to generate the margins regarding different training triplets; (iii) explicit user-user/item-item similarity modeling is incorporated in the objective function. The Wasserstein distance is employed to determine preferences because it obeys the triangle inequality and can measure the distance between probabilistic distributions. Via a comparison using five real-world datasets with state-of-the-art methods, the proposed model outperforms the best existing models by 4-22% in terms of recall@K on Top-K recommendation.