Trust Decay-Based Temporal Learning for Dynamic Recommender Systems With Concept Drift Adaptation

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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Bibliographic Details
Main Authors: Hartatik, Lukman Heryawan, Reza Pulungan
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Temporal recommendation
user modeling
concept drift
BiGRU autoencoder
trust region optimization
Online Access:https://ieeexplore.ieee.org/document/11050419/
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https://ieeexplore.ieee.org/document/11050419/

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