Affiliation:
1. Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China
2. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
Abstract
With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still warranted. The main focus of this paper is on recommendation models that incorporate deep learning techniques. The objective is to guide novice researchers interested in this field through the investigation and application of the proposed recommendation models. Specifically, we first categorize existing RS approaches into four types: content-based recommendations, sequence recommendations, cross-domain recommendations, and social recommendation methods. We then introduce the definitions and address the challenges associated with these RS methodologies. Subsequently, we propose a comprehensive categorization framework and novel taxonomies for these methodologies, providing a thorough account of their research advancements. Finally, we discuss future developments regarding this topic.
Funder
Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
National Key R&D Program of China
Beijing Nova Program from Beijing Municipal Science & Technology Commission
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference156 articles.
1. Recommending learning objects through attentive heterogeneous graph convolution and operation-aware neural network;Zhu;IEEE Trans. Knowl. Data Eng.,2021
2. Guidelines for the analysis and design of argumentation-based recommendation systems;Leiva;IEEE Intell. Syst.,2020
3. Eigentaste: A constant time collaborative filtering algorithm;Goldberg;Inf. Retr.,2001
4. Comprehensive review of artificial neural network applications to pattern recognition;Abiodun;IEEE Access,2019
5. A survey on deep learning in big data;Gheisari;Proceedings of the 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC),2017
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