Trust Decay-Based Temporal Learning for Dynamic Recommender Systems With Concept Drift Adaptation
Modeling temporal dynamics in recommendation systems is essential for capturing drifts in user preferences over time, particularly under conditions of data sparsity and non-stationarity. In this study, we propose a novel framework, Trust Decay-based Temporal Learning (TDTL), which integrates trust r...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11050419/ |
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