AutoRec++: Incorporating Debias Methods into Autoencoder-based Recommender System

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Cheng Liang, Yi He, Teng Huang, Di Wu
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引用次数: 0

Abstract

The deep neural network-based (DNN-based) model has proven powerful in user data behavior representation, efficiently implementing a recommender system (RS). Most prior works focus on developing a sophisticated architecture to better-fit user data. However, user behavior data are commonly collected from multiple scenarios and generated by numerous users, resulting in various biases existing in these data. Unfortunately, prior DNN-based RSs dealing with these biases are fragmented and lack a comprehensive solution. This paper aims to comprehensively handle these biases in user behavior data in preprocessing stage and training state. By incorporating the preprocessing bias (PB) and training bias (TB) into the representative autoencoder-based AutoRec model, we proposed AutoRec++. Experimental results in five commonly used benchmark datasets demonstrate that: 1) the basic model’s preference can boost by the optimal PB and TB combinations, and 2) our proposed AutoRec++ reaches a better prediction accuracy than DNN-based and non-DNN-based state-of-the-art models.
AutoRec++:将Debias方法集成到基于自动编码器的推荐系统中
基于深度神经网络(DNN-based)的模型在用户数据行为表示方面已经被证明是强大的,可以有效地实现推荐系统(RS)。大多数先前的工作都集中在开发一个复杂的架构来更好地适应用户数据。然而,用户行为数据通常是从多个场景中收集的,由众多用户生成,导致这些数据存在各种偏差。不幸的是,先前基于dnn的RSs处理这些偏差是分散的,缺乏全面的解决方案。本文旨在综合处理用户行为数据在预处理阶段和训练阶段的这些偏差。通过将预处理偏差(PB)和训练偏差(TB)结合到具有代表性的基于自编码器的AutoRec模型中,我们提出了AutoRec++。在五个常用的基准数据集上的实验结果表明:1)最优的PB和TB组合可以提高基本模型的偏好;2)我们提出的AutoRec++比基于dnn和非dnn的现有模型具有更好的预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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