Cross-domain recommendation aims to integrate data from multiple domains and introduce information from source domains, thereby achieving good recommendations on the target domain. Recently, contrastive learning has been introduced into the cross-domain recommendations and has obtained some better results. However, most cross-domain recommendation algorithms based on contrastive learning suffer from the bias problem. In addition, the correlation between the user’s single-domain and cross-domain preferences is not considered. To address these problems, a new recommendation model is proposed for cross-domain scenarios based on intra-domain and inter-domain contrastive learning, which aims to obtain unbiased user preferences in cross-domain scenarios and improve the recommendation performance of both domains. Firstly, a network enhancement module is proposed to capture users’ complete preference by applying a graphical convolution and attentional aggregator. This module can reduce the limitations of only considering user preferences in a single domain. Then, a cross-domain infomax objective with noise contrast is presented to ensure that users’ single-domain and cross-domain preferences are correlated closely in sequential interactions. Finally, a joint training strategy is designed to improve the recommendation performances of two domains, which can achieve unbiased cross-domain recommendation results. At last, extensive experiments are conducted on two real-world cross-domain scenarios. The experimental results show that the proposed model in this paper achieves the best recommendation results in comparison with existing models.

跨领域推荐旨在整合多个领域的数据，引入源领域的信息，从而在目标领域获得良好的推荐。最近，对比学习被引入到跨领域推荐中，并取得了一些更好的结果。然而，大多数基于对比学习的跨领域推荐算法都存在偏差问题。另外，没有考虑用户单域和跨域偏好之间的相关性。针对这些问题，基于域内和域间对比学习，提出了一种新的跨域场景推荐模型，旨在获得跨域场景中无偏的用户偏好，提高两个域的推荐性能。首先，提出了一个网络增强模块，通过应用图形卷积和注意力聚合器来捕获用户的完整偏好。该模块可以减少仅考虑单个域中的用户偏好的限制。然后，提出了具有噪声对比的跨域infomax目标，以确保用户的单域和跨域偏好在顺序交互中紧密相关。最后，设计联合训练策略来提高两个域的推荐性能，从而达到无偏的跨域推荐结果。最后，对两个真实的跨域场景进行了广泛的实验。实验结果表明，与现有模型相比，本文提出的模型取得了最好的推荐结果。