Multi-View Intent Disentangle Graph Networks for Bundle Recommendation
Authors: Sen Zhao, Wei Wei, Ding Zou, Xianling Mao4379-4387
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on two benchmark datasets demonstrate that MIDGN outperforms the state-of-the-art methods by over 10.7% and 26.8%, respectively. |
| Researcher Affiliation | Collaboration | 1 Cognitive Computing and Intelligent Information Processing (CCIIP) Laboratory, School of Computer Science and Technology, Huazhong University of Science and Technology 2 Beijing Institute of Technology 3 Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL) |
| Pseudocode | No | The paper describes methods using mathematical equations and textual descriptions but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Two datasets are used to evaluate our proposed method. The one is Net Ease provided by the work(Cao et al. 2017). The other dataset is named Youshu provided by the work (Chen et al. 2019). |
| Dataset Splits | Yes | We hold out the latest 10 bundles of each user for testing and the latest 5 bundles of each user for validation. |
| Hardware Specification | Yes | We make use of Nvidia Titan RTX graphics card equipped with AMD r9-5900x CPU (32GB Memory). |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The negative sampling is set to 1, the learning rate is selected from 1e-5, 3e-5, 1e-4, 3e-4, 1e-3, 3e-3, and we adopt BPR loss for all methods and employ Adam optimizer with the 4096-size mini-batch and fit the embedding size as 64. For MIDGN, the number of intents and the number of layers are selected from 1, 2, 4, 8 and 1, 2, 3, 4, respectively |