Domain-Aware Cross-Attention for Cross-domain Recommendation
This addresses the challenge of sparse data in recommender systems for industrial applications, though it appears incremental as it builds on existing cross-domain methods.
The paper tackles the problem of cross-domain recommendation by introducing a domain-aware cross-attention method to improve performance in sparse target domains, achieving significant improvements in Click-Through-Rate (CTR) and effective cost per mille (ECPM) in online deployment.
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).