A Comprehensive Survey of Recommender Systems Based on Deep Learning
Abstract
:1. Introduction
- We present a comprehensive examination of recommender systems, with a specific emphasis on their integration with deep learning. We categorize them in terms of their developmental perspective, providing a comprehensive view of the evolution of the recommender systems field.
- We conduct a review of the research progress of recommender systems integrated with deep learning, focusing on methods for applying deep learning to collaborative filtering. Specifically, we perform a comprehensive analysis of four recommendation approaches that incorporate deep learning: content-based recommendation, sequence recommendation, cross-domain recommendation, and social recommendation.
- We identify future research directions in the field of deep learning-based recommender systems, contributing to the advancement of the research community.
2. Related Work
3. Overview of the Recommender Systems
3.1. Content-Based Recommendation
- Personalized recommendations: These recommendations are based on the user’s historical interests, ensuring that the recommended content aligns with the user’s preferences.
- Simple principle with strong interpretability: Content-based recommendations can be made based on label dimensions or by embedding items into a vector space using similarity, making this strategy easy to implement. It is also readily accepted and validated by users.
- Addresses the cold-start problem to some extent: As long as sufficient content attributes are available, new items can be effectively handled without relying on other users’ behaviors.
3.2. Sequential Recommendation
3.2.1. Standard Sequence Recommendation
3.2.2. Long- and Short-Term Sequence Recommendation
3.2.3. Multi-Objective Sequence Recommendation
3.3. Cross-Domain Recommendation
3.3.1. Single-Target CDR
3.3.2. Dual-Target CDR
3.3.3. Multi-Target CDR
3.4. Social Recommendation
3.4.1. Traditional Collaborative Filtering-Based Social Recommender Systems
3.4.2. Deep Social Recommendation Based on Graph Embedding
3.4.3. Social Recommendation Based on GNN
4. Challenges and Developments
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Zhou, H.; **ong, F.; Chen, H. A Comprehensive Survey of Recommender Systems Based on Deep Learning. Appl. Sci. 2023, 13, 11378. https://doi.org/10.3390/app132011378
Zhou H, **ong F, Chen H. A Comprehensive Survey of Recommender Systems Based on Deep Learning. Applied Sciences. 2023; 13(20):11378. https://doi.org/10.3390/app132011378
Chicago/Turabian StyleZhou, Hongde, Fei **ong, and Hongshu Chen. 2023. "A Comprehensive Survey of Recommender Systems Based on Deep Learning" Applied Sciences 13, no. 20: 11378. https://doi.org/10.3390/app132011378