# Your decision path does matter in pre-training industrial recommenders with multi-source behaviors

arXiv: https://arxiv.org/abs/2405.17132 · HTML: https://mlsanity.com/paper/2405.17132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 17 of 17)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Learning Sparse Neural Networks through L0 Regularization](https://mlsanity.com/paper/1712.01312/) (2017)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2112.08679/) (2021)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems](https://mlsanity.com/paper/2302.03735/) (2023)
- [A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions](https://mlsanity.com/paper/2108.03357/) (2021)
- [One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation](https://mlsanity.com/paper/2211.11964/) (2022)
- [Disentangled Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2201.03482/) (2022)
- [Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning](https://mlsanity.com/paper/2201.05973/) (2022)
- [M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation](https://mlsanity.com/paper/2304.07911/) (2023)

## Abstract (arXiv)

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.