Cross-domain Recommendation Without Sharing User-relevant Data

By Chen Gao, Xiangning Chen, Fuli Feng, Kai Zhao, Xiangnan He, Yong Li, Depeng Jin. 2019

146 citations recorded by the source corpus.

Web systems that provide the same functionality usually share a certain amount of items. This makes it possible to combine data from different websites to improve recommendation quality, known as the cross-domain recommendation task. Despite many research efforts on this task, the main drawback is that they largely assume the data of different systems can be fully shared. Such an assumption is unrealistic - different systems are typically operated by different companies, and it may violate business privacy policy to directly share user behavior data since it is highly sensitive. In this work, we consider a more practical scenario to perform cross-domain recommendation. To avoid the leak of user privacy during the data sharing process, we consider sharing only the information of the item side, rather than user behavior data. Specifically, we transfer the item embeddings across domains, making it easier for two companies to reach a consensus (e.g., legal policy) on data sharing since the data to be shared is user-irrelevant and has no explicit semantics. To distill useful signals from transferred item embeddings, we rely on the strong representation power of neural networks and develop a new method named as NATR (short for Neural Attentive Transfer Recommendation). We perform extensive experiments on two real-world datasets, demonstrating that NATR achieves similar or even better performance than traditional cross-domain recommendation methods that directly share user-relevant data. Further insights are provided on the efficacy of NATR in using the transferred item embeddings to alleviate the data sparsity issue.

Subfields: Artificial Intelligence, Computer Networks and Communications, 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. Yong Li
2. Chen Gao
3. Depeng Jin
4. Xiangnan He
5. Yu Zheng
6. Jianxin Chang
7. Nian Li
8. Jinghua Piao

In Computer Networks and Communications

1. Yong Li
2. Xiangnan He
3. Tat-seng Chua

In Information Systems

1. Yong Li
2. Chen Gao
3. Depeng Jin
4. Xiangnan He
5. Yu Zheng
6. Jianxin Chang
7. Nian Li
8. Jinghua Piao

Citations in the corpus

• Cross-Domain Recommendation: Challenges, Progress, and Prospects (2021, background): “Thus, it is urgent to build CDR meanwhile protect data privacy [ Gao et al. , 2019 ] .”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, background): “As an increasing number of users interact with multimodal information across multiple domains, cross-domain recommendation (CDR) has been demonstrated to be a promising method to alleviate cold start and data sparsity problems [50, 52, 70, 79, 124, 132, 238].”