  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.
