Memory Augmented Graph Neural Networks for Sequential Recommendation
Authors: Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates5045-5052
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate our model on five real-world datasets, comparing with several state-of-the-art methods and using a variety of performance metrics. The experimental results demonstrate the effectiveness of our model for the task of Top-K sequential recommendation. |
| Researcher Affiliation | Collaboration | Chen Ma, 1 Liheng Ma, 1,3 Yingxue Zhang,2 Jianing Sun,2 Xue Liu,1 Mark Coates1 1Mc Gill University, 2Huawei Noah s Ark Lab in Montreal, 3Mila |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The proposed model is evaluated on five real-world datasets from various domains with different sparsities: Movie Lens20M (Harper and Konstan 2016), Amazon-Books and Amazon-CDs (He and Mc Auley 2016b), Goodreads Children and Goodreads-Comics (Wan and Mc Auley 2018). |
| Dataset Splits | Yes | For each user, we use the earliest 70% of the interactions in the user sequence as the training set and use the next 10% of interactions as the validation set for hyper-parameter tuning. The remaining 20% constitutes the test set for reporting model performance. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In the experiments, the latent dimension of all the models is set to 50. For GRU4Rec and GRU4Rec+, we find that a learning rate of 0.001 and batch size of 50 can achieve good performance. ... For MA-GNN, we follow the same setting in Caser to set |L| = 5 and |T| = 3. ... The embedding size d is also set to 50. The value of h and m are selected from {5, 10, 15, 20}. The learning rate and λ are set to 0.001 and 0.001, respectively. The batch size is set to 4096. |