Dynamic Multi-Interest Graph Neural Network for Session-Based Recommendation
Authors: Mingyang Lv, Xiangfeng Liu, Yuanbo Xu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three bench-mark datasets demonstrate that our methods achieve better performance on different metrics. We conducted an evaluation of the proposed method on three real-world benchmark datasets. We conducted an ablation study on each design choice in DMI-GNN |
| Researcher Affiliation | Academia | 1MIC Lab, College of Computer Science and Technology, Jilin University, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using prose and mathematical equations but does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/MICLab-Rec/DMI-GNN |
| Open Datasets | Yes | We conducted an evaluation of the proposed method on three real-world benchmark datasets. The Tmall1 dataset, sourced from the IJCAI-15 competition... The Last FM2 dataset... The Retail Rocket3 dataset... 1https://tianchi.aliyun.com/dataset/data Detail?data Id=42 2http://ocelma.net/Music Recommendation Dataset/lastfm1K.html 3https://www.kaggle.com/retailrocket/ecommerce-dataset |
| Dataset Splits | Yes | For a fair comparison, we follow the preprocessing method proposed by SR-GNN (Wu et al. 2019). The statistics of the three datasets after preprocessing are detailed in Table 1. Table 1: # training # test # items Avg.Lens Tmall 351,268 25,898 40,727 6.69 Retail Rocket 433,643 15,132 36,968 5.43 Last FM 2,837,330 672,833 38,615 11.78 |
| Hardware Specification | Yes | We conducted the experiment on a NVIDIA 3080Ti, using Py Torch version 1.11.0 + cu113. |
| Software Dependencies | Yes | We conducted the experiment on a NVIDIA 3080Ti, using Py Torch version 1.11.0 + cu113. |
| Experiment Setup | Yes | For fair comparison, we aligned our experimental settings with those of GCE-GNN. The Adam optimizer (Kingma and Ba 2015) was chosen, operating at a learning rate of 0.001. Our model was configured with an embedding size of 100 and trained within 20 epochs, processing data in batches of 100. For DMI-GNN, we tune the balance coefficient β among {0.001, 0.005, 0.01, 0.05}, U among {2, 3, 4, 5}, and searched η from 8 to 18 in 2 increments. |