Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Dynamic Multi-Interest Graph Neural Network for Session-Based Recommendation
Authors: Mingyang Lv, Xiangfeng Liu, Yuanbo Xu
AAAI 2025 | Venue PDF | 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. |