Cross-domain recommendation has attracted substantial interest in industrial apps such as Meituan, which serves multiple business domains via knowledge transfer and meets the diverse interests of users. However, existing methods typically follow an implicit modeling paradigm that blends the knowledge from both the source and target domains, and design intricate network structures to share learned embeddings or patterns between domains to improve recommendation accuracy. Since the transfer of interest signals is unsupervised, these implicit paradigms often struggle with the negative transfer resulting from differences in service functions and presentation forms across different domains. In this paper, we propose a simple and effective EXplicit Interest Transfer framework named EXIT to address the stated challenge. Specifically, we propose a novel label combination approach that enables the model to directly learn beneficial source domain interests through supervised learning, while excluding inappropriate interest signals. Moreover, we introduce a scene selector network to model the interest transfer intensity under fine-grained scenes. Offline experiments conducted on the industrial production dataset and online A/B tests validate the superiority and effectiveness of our proposed framework. Without complex network structures or training processes, EXIT can be easily deployed in the industrial recommendation system. EXIT has been successfully deployed in the online homepage recommendation system of Meituan App, serving the main traffic.

翻译：跨域推荐在美团等工业应用中引起了广泛关注，其通过知识迁移服务于多个业务领域，满足用户的多样化兴趣。然而，现有方法通常遵循一种隐式建模范式，将源域和目标域的知识混合，并设计复杂的网络结构以在域间共享学习到的嵌入或模式，从而提高推荐准确性。由于兴趣信号的迁移是无监督的，这些隐式范式常常难以应对因不同领域间服务功能和呈现形式差异导致的负迁移问题。本文提出了一种简单有效的显式兴趣迁移框架EXIT，以应对上述挑战。具体而言，我们提出了一种新颖的标签组合方法，使模型能够通过监督学习直接学习有益的源域兴趣，同时排除不合适的兴趣信号。此外，我们引入了一个场景选择器网络，以建模细粒度场景下的兴趣迁移强度。在工业生产数据集上进行的离线实验以及在线A/B测试验证了我们所提框架的优越性和有效性。无需复杂的网络结构或训练过程，EXIT可以轻松部署在工业推荐系统中。EXIT已成功部署在美团App的在线首页推荐系统中，服务于主要流量。