Domain-Aware Cross-Attention for Cross-domain Recommendation

• URL: http://arxiv.org/abs/2401.11705v1
• Date: Mon, 22 Jan 2024 06:12:48 GMT
• Title: Domain-Aware Cross-Attention for Cross-domain Recommendation
• Authors: Yuhao Luo and Shiwei Ma and Mingjun Nie and Changping Peng and Zhangang Lin and Jingping Shao and Qianfang Xu
• Abstract summary: Cross-domain recommendation (CDR) is an important method to improve recommender system performance. We introduce a two-step domain-aware cross-attention, extracting transferable features of the source domain from different granularity. We conduct experiments on both public datasets and industrial datasets, and the experimental results demonstrate the effectiveness of our method.
• Score: 4.602115311495822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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).

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