奇异值分解
初始化
计算机科学
协同过滤
矩阵分解
RSS
推荐系统
嵌入
人工神经网络
趋同(经济学)
算法
数据挖掘
机器学习
人工智能
操作系统
物理
特征向量
经济
程序设计语言
量子力学
经济增长
作者
Tianlin Huang,Rujie Zhao,Lvqing Bi,Defu Zhang,Chengpeng Lu
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2022-10-01
卷期号:33 (10): 6021-6029
被引量:14
标识
DOI:10.1109/tnnls.2021.3070853
摘要
Singular value decomposition (SVD) is one of the most effective algorithms in recommender systems (RSs). Due to the iterative nature of SVD algorithms, one big challenge is initialization that has a major impact on the convergence and performance of RSs. Unfortunately, existing SVD algorithms in the literature typically initialize the user and item features in a random manner; thus, data information is not fully utilized. This work addresses the challenge of developing an efficient initialization method for SVD algorithms. We propose a general neural embedding initialization framework, where a low-complexity probabilistic autoencoder neural network initializes the features of user and item. This framework supports explicit and implicit feedback data sets. The design details of our proposed framework are elaborated and discussed. Experimental results show that RSs based on our proposed initialization framework outperform the state-of-the-art methods in rating prediction. Moreover, regarding item ranking, our proposed framework shows an improvement of at least 2.20% ~5.74% than existing SVD algorithms and other matrix factorization methods in the literature.
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