An Evolving Preference-Based Recommendation System

被引:3
作者
Chen, Yi-Cheng [1 ]
Lee, Wang-Chien [2 ]
机构
[1] Natl Cent Univ, Dept Informat Management, Taoyuan 320, Taiwan
[2] Penn State Univ, Dept Comp Sci & Engn, State Coll, PA 16801 USA
来源
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE | 2024年 / 8卷 / 02期
关键词
Deep learning; recommendation system; matrix factorization; transformer; MATRIX FACTORIZATION; MODEL;
D O I
10.1109/TETCI.2023.3343998
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this "Info-plosion" era, recommendation systems (or recommenders) play a significant role in finding interesting items in a surge of the on-line digital activity and e-commerce. Because of its practicability, the matrix factorization (MF) technique has been widely applied for recommendation systems. Prior MF-based studies on recommendations generally extract latent factors from users and items to make recommendations. However, user's preferences may change over time in real-world applications. In this paper, by integrating the transformer and matrix factorization techniques, a novel recommendation system, namely Evolution-Based Transformer Recommendation (Evo-TransRec), is developed to effectively describe the evolution of user preferences over time. Several optimization techniques are equipped to Evo-TransRec to capture the evolution relations and predict the user preference. The experimental results show that Evo-TransRec outperforms all the state-of-the-art baselines on real datasets to demonstrate the practicability.
引用
收藏
页码:1118 / 1124
页数:7
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