Domain-Aware Cross-Attention for Cross-domain Recommendation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913203107135488 |
|---|---|
| author | Luo, Yuhao Ma, Shiwei Nie, Mingjun Peng, Changping Lin, Zhangang Shao, Jingping Xu, Qianfang |
| author_facet | Luo, Yuhao Ma, Shiwei Nie, Mingjun Peng, Changping Lin, Zhangang Shao, Jingping Xu, Qianfang |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11705 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain-Aware Cross-Attention for Cross-domain Recommendation Luo, Yuhao Ma, Shiwei Nie, Mingjun Peng, Changping Lin, Zhangang Shao, Jingping Xu, Qianfang Information Retrieval Artificial Intelligence 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). |
| title | Domain-Aware Cross-Attention for Cross-domain Recommendation |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2401.11705 |