An End-to-End Personalized Preference Drift Aware Sequential Recommender System With Optimal Item Utilization

An End-to-End Personalized Preference Drift Aware Sequential Recommender System With Optimal Item Utilization

The user preference is dynamic and requires drift detection to capture changes for delivering relevant recommendations. A sequential recommender system with drift detection was proposed, where drift points are indicated by comparing characteristics of consecutive items. The model leverages drift poi...

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Bibliographic Details
Main Authors: Saranya Maneeroj, Nakarin Sritrakool
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Neural networks
machine learning
recommender systems
Online Access:https://ieeexplore.ieee.org/document/9794734/

Internet

https://ieeexplore.ieee.org/document/9794734/

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