Domain-Aware Cross-Attention for Cross-domain Recommendation
Yuhao Luo, Shiwei Ma, Mingjun Nie, Changping Peng, Zhangang Lin, Jingping Shao, Qianfang Xu
TL;DR
The paper tackles cold-start in cross-domain recommendations by leveraging data-rich source domains to assist sparse target domains. It introduces Domain-Aware Cross-Attention (DACDR), a two-step attention mechanism that selects transferable knowledge from the source domain using domain-level and item-level cues. A domain encoder generates transformed target embeddings end-to-end, enabling simple fine-tuning for new domains. Experiments on public and industrial data show consistent CTR and ECPM gains, and a live deployment confirms practical impact.
Abstract
Cross-domain recommendation (CDR) is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing cross-domain recommendations fail to fully utilize the target domain's special features and are hard to be generalized to new domains. The designed network is complex and is not suitable for rapid industrial deployment. Our method introduces a two-step domain-aware cross-attention, extracting transferable features of the source domain from different granularity, which allows the efficient expression of both domain and user interests. In addition, we simplify the training process, and our model can be easily deployed on new domains. We conduct experiments on both public datasets and industrial datasets, and the experimental results demonstrate the effectiveness of our method. We have also deployed the model in an online advertising system and observed significant improvements in both Click-Through-Rate (CTR) and effective cost per mille (ECPM).