This paper presents a systematic study of how to enhance recommender systems under volatile user interest drifts. A key development challenge along this ...
[PDF] Enhancing Recommender Systems Under Volatile User Interest Drifts
staff.ustc.edu.cn › p1257-cao
Nov 6, 2009 · We propose a general algorithm framework based on interest drift pattern detection. This framework is suitable for most existing recommender ...
This paper presents a systematic study of how to enhance recommender systems under volatile user interest drifts. A key development challenge along this ...
Nov 2, 2009 · This paper presents a systematic study of how to enhance recommender systems under volatile user interest drifts.
Enhancing recommender systems under volatile userinterest drifts ; Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, ...
People also ask
How to improve recommendation systems?
What machine learning methods can be used to improve the recommendation system?
What are the six types of recommendation systems?
What are the key problems faced while designing a recommendation system?
This paper presents a systematic study of how to enhance recommender systems under volatile user interest drifts. A key development challenge along this line is ...
This paper presents a systematic study of how to enhance recommender systems under volatile user interest drifts. A key development challenge along this ...
Detection of the customer time-variant pattern for improving recommender systems ... Enhancing recommender systems under volatile userinterest drifts. J. Chen et ...
This research proposes preference relaxation as an alternative to existing similarity-based product recommendation agents used in such context. Building on ...
A fuzzy user-interest drift detection based recommender system that adapts to user- interest drift and improves prediction accuracy and the results show ...