A Deep Framework for Cross-Domain and Cross-System Recommendations

By Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Ali Orgun, Jia Wu. 2018

177 citations recorded by the source corpus.

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from the source domain or system to improve the recommendation accuracy in the target domain or system. Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy. However, this is a very challenging task because of the complex relationships between the latent factors of the source and target domains or systems. To this end, in this paper, we propose a Deep framework for both Cross-Domain and Cross-System Recommendations, called DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep Neural Network (DNN). Specifically, DCDCSR first employs the MF models to generate user and item latent factors and then employs the DNN to map the latent factors across domains or systems. More importantly, we take into account the rating sparsity degrees of individual users and items in different domains or systems and use them to guide the DNN training process for utilizing the rating data more effectively. Extensive experiments conducted on three real-world datasets demonstrate that DCDCSR framework outperforms the state-of-the-art CDR and CSR approaches in terms of recommendation accuracy.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jun Zhou
2. Keyu Duan
3. Dongbo Xi
4. Qing He
5. Hui Xiong
6. Jie Lu
7. Yuan Qi
8. Xiaolin Zheng

In Information Systems

1. Qing He
2. Hui Xiong
3. Jie Lu
4. Yuan Qi
5. Xiaolin Zheng
6. Chaochao Chen
7. Fuzhen Zhuang
8. Hengshu Zhu

Citations in the corpus

• A Comprehensive Survey on Transfer Learning (2019, background): “[199] proposed a deep framework, which first generates the user and item feature representations based on the matrix factorization technique, and then employs a deep neural network to learn the mapping of features across domains.”
• Artificial intelligence in recommender systems (2020, methodology): “A framework for CDRS on partially overlapping entities with a deep neural net-123 work is proposed in [125].”
• Practical Privacy Preserving POI Recommendation (2020, methodology): “Deep neural network (DNN) has also been widely used due to its powerful representation ability [65, 67].”
• Cross-Domain Recommendation: Challenges, Progress, and Prospects (2021, methodology): “In the literature [Zhu et al. , 2018; Zhu et al. , 2020], this type of cross-domain recommendation is also referred to as ‘cross-system recommendation’.”