Summary of Your Decision Path Does Matter in Pre-training Industrial Recommenders with Multi-source Behaviors, by Chunjing Gan et al.
Your decision path does matter in pre-training industrial recommenders with multi-source behaviors
by Chunjing Gan, Binbin Hu, Bo Huang, Ziqi Liu, Jian Ma, Zhiqiang Zhang, Wenliang Zhong, Jun Zhou
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Hierarchical decIsion path Enhanced Representation (HIER) model aims to improve cross-domain recommendation by taking into account the decision paths users take when interacting with online service platforms. The approach leverages graph neural networks to capture high-order topological information from knowledge graphs and adaptively learns decision paths through contrastive learning. Experimental results demonstrate the superiority of HIER in both online and offline environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to improve recommendations on online service platforms by considering how users make decisions. The approach uses special computer programs called graph neural networks to understand relationships between different types of user behavior. It also adapts to individual user decision-making patterns, which helps improve the quality of recommended services. The method performs well in both real-world and simulated environments. |
