This is the implementation of our two recent models for recommending scientific articles. The first model, CATA, utilizes articles’ titles and abstracts via a single attentive autoencoder, while the second model, CATA++, utilizes additional articles' information (such as tags and citations between articles) via two parallel attentive autoencoders.
- Collaborative Attentive Autoencoder for Scientific Article Recommendation (ICMLA 2019)
- CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles (arXiv 2020)
| Dataset | #users | #articles | #pairs | sparsity |
|---|---|---|---|---|
| Citeulike-a | 5,551 | 16,980 | 204,986 | 99.78% |
| Citeulike-t | 7,947 | 25,975 | 134,860 | 99.93% |
- Python 3
- Tensorflow
- Keras
You can evaluate our models with different settings in terms of:
- data_name
-d: 'a' for citeulike-a and 't' for citeulike-t. [Optional] [Default=citeulike-a] - sparse
-s: '0', 'no', 'false', or 'f' for dense. [Optional] [Default=sparse] - pretrain
-pretrain: '1', 'yes', 'true' or 't' for pretrain the attentive autoencoder. [Optional] [Default=No-pretraining] - epochs
-e: Number of epochs to pretrain the attentive autoencoder. [Optional] [Default=150] - lambda_u
-u: value of lambda_u. [Optional] [Default=10] - lambda_v
-v: value of lambda_v. [Optional] [Default=0.1] - pmf_epochs
-pe: Number of iterations for PMF. [Optional] [Default=100] - output_name
-o: Name of the output file. [Optional] - latent_size
-l:Size of latent space. [Optional] [Default=50]
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To run CATA model, use test_CATA.py file.
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To run CATA++ model, use test_CATA++.py file.
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Example 1: Run CATA++ model for citeulike-t dataset with the dense setting. Also, always pretrain the two autoencoders if you run the code for the first time by setting
-pretrain 1:python3 test_CATA++.py -pretrain 1 -d 't' -s 0
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Example 2: Run CATA++ model for citeulike-t dataset with the sparse setting:
python3 test_CATA++.py -pretrain 0 -d 't' -s 1
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Example 3: Run CATA model for citeulike-a dataset with the sparse setting:
python3 test_CATA.py -pretrain 0 -d 'a' -s 1