Your decision path does matter in pre-training industrial recommenders with multi-source behaviors
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913364887732224 |
|---|---|
| author | Gan, Chunjing Hu, Binbin Huang, Bo Liu, Ziqi Ma, Jian Zhang, Zhiqiang Zhong, Wenliang Zhou, Jun |
| author_facet | Gan, Chunjing Hu, Binbin Huang, Bo Liu, Ziqi Ma, Jian Zhang, Zhiqiang Zhong, Wenliang Zhou, Jun |
| contents | Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in. Aiming at effective content delivery, cross-domain recommendation are introduced to learn high-quality representations by transferring behaviors from data-rich scenarios. However, these methods overlook the impact of the decision path that users take when conduct behaviors, that is, users ultimately exhibit different behaviors based on various intents. To this end, we propose HIER, a novel Hierarchical decIsion path Enhanced Representation learning for cross-domain recommendation. With the help of graph neural networks for high-order topological information of the knowledge graph between multi-source behaviors, we further adaptively learn decision paths through well-designed exemplar-level and information bottleneck based contrastive learning. Extensive experiments in online and offline environments show the superiority of HIER. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17132 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Your decision path does matter in pre-training industrial recommenders with multi-source behaviors Gan, Chunjing Hu, Binbin Huang, Bo Liu, Ziqi Ma, Jian Zhang, Zhiqiang Zhong, Wenliang Zhou, Jun Machine Learning Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in. Aiming at effective content delivery, cross-domain recommendation are introduced to learn high-quality representations by transferring behaviors from data-rich scenarios. However, these methods overlook the impact of the decision path that users take when conduct behaviors, that is, users ultimately exhibit different behaviors based on various intents. To this end, we propose HIER, a novel Hierarchical decIsion path Enhanced Representation learning for cross-domain recommendation. With the help of graph neural networks for high-order topological information of the knowledge graph between multi-source behaviors, we further adaptively learn decision paths through well-designed exemplar-level and information bottleneck based contrastive learning. Extensive experiments in online and offline environments show the superiority of HIER. |
| title | Your decision path does matter in pre-training industrial recommenders with multi-source behaviors |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.17132 |