wehub-resource-sync
85 lines
2.9 KiB
Markdown
85 lines
2.9 KiB
Markdown
# DGL Implementation of the TAHIN
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This DGL example implements the TAHIN module proposed in the paper [HCDIR](https://arxiv.org/pdf/2007.15293.pdf). Since the code and dataset have not been published yet, we implement its main idea and experiment on two other datasets.
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Example implementor
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----------------------
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This example was implemented by [KounianhuaDu](https://github.com/KounianhuaDu) during her software development intern time at the AWS Shanghai AI Lab.
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Dependencies
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- pytorch 1.7.1
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- dgl 0.6.0
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- scikit-learn 0.22.1
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Datasets
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The datasets used can be downloaded from [here](https://github.com/librahu/HIN-Datasets-for-Recommendation-and-Network-Embedding). For the experiments, all the positive edges are fetched and the same number of negative edges are randomly sampled. The edges are then shuffled and splitted into train/validate/test at a ratio of 6:2:2. The positive edges that appear in the validation and test sets are then removed from the original graph.
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The original graph statistics:
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**Movielens**
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(Source : https://grouplens.org/datasets/movielens/)
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| Entity |#Entity |
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| :-------------:|:-------------:|
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| User | 943 |
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| Age | 8 |
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| Occupation | 21 |
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| Movie | 1,682 |
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| Genre | 18 |
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| Relation |#Relation |
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| :-------------: |:-------------:|
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| User - Movie | 100,000 |
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| User - User (KNN) | 47,150 |
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| User - Age | 943 |
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| User - Occupation | 943 |
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| Movie - Movie (KNN) | 82,798 |
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| Movie - Genre | 2,861 |
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**Amazon**
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(Source : http://jmcauley.ucsd.edu/data/amazon/)
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| Entity |#Entity |
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| :-------------:|:-------------:|
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| User | 6,170 |
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| Item | 2,753 |
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| View | 3,857 |
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| Category | 22 |
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| Brand | 334 |
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| Relation |#Relation |
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| :-------------: |:-------------:|
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| User - Item | 195,791 |
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| Item - View | 5,694 |
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| Item - Category | 5,508 |
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| Item - Brand | 2,753 |
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How to run
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```python
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python main.py --dataset amazon --gpu 0
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```
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```python
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python main.py --dataset movielens --gpu 0
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```
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Performance
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**Results**
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| Dataset | Movielens | Amazon |
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|---------| ------------------------ | ------------------------ |
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| Metric | HAN / TAHIN | HAN / TAHIN |
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| AUC | 0.9297 / 0.9392 | 0.8470 / 0.8442 |
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| ACC | 0.8627 / 0.8683 | 0.7672 / 0.7619 |
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| F1 | 0.8631 / 0.8707 | 0.7628 / 0.7499 |
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| Logloss | 0.3689 / 0.3266 | 0.5311 / 0.5150 |
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