😆 CARAR: Correlation-aware Review Aspect Recommender System

This is the MATLAB implemention of our paper:

T. Wei, T. W. S Chow and J. Ma, “Modeling Self-representation Label Correlations for Textual Aspects and Emojis Recommendation”, in IEEE Transactions on Neural Networks and Learning Systems, 2022. Paper link

File

The proposed Correlation-aware Review Aspect Recommender System (CARAR) includes three detailed steps. All corresponding codes are stored in Model/:

1. Model/CARAR_C.m : Self-representation correlation mapping.
2. Model/CARAR_LF.m : Latent factors updating.
3. Model/CARAR_W.m : Incorporaing additional information.

Each function above is called by Model/CARAR.m to formulate the full CARAR model.

Datasets

• Yelp ¹
• Beeradvocate ²
• HotelRec ³
• OpenRice ⁴ Text / Emoji

All datasets are stored in Data/ in .mat Matlab workspace files. Each file include:

• D (N * d): Additional information for each record.
• E (N * l): Review aspect labels for each record.
• is (N * 1): Item ID for each record.
• iu (N * 1): User ID for each record.
• Label_E (Only available for OpenRice Text / OpenRice Emoji): Descriptions for each review aspect.

You can download the datasets we used at Google Drive. Extract the downloaded zip file and put them in Data/.

Usage

Environment

• MathWorks MATLAB R2019b or laber
  • Parallel Computing Toolbox required
• This code is designed to run in a GPU environment and is not adapted for environments without a GPU.

Running demos

Specify the dataset in demo.m and run the script. The best hyperparameters will be loaded automatically to run the demo.

If you like to choose different hyperparameters, change them manually in Model/CARAR.m.

How to cite

@ARTICLE{9774027,
  author={Wei, Tianjun and Chow, Tommy W. S. and Ma, Jianghong},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={Modeling Self-Representation Label Correlations for Textual Aspects and Emojis Recommendation}, 
  year={2022},
  volume={},
  number={},
  pages={1-13},
  doi={10.1109/TNNLS.2022.3171335}
}

References

Footnotes

1. Yelp Dataset ↩

2. J. McAuley, J. Leskovec, and D. Jurafsky, “Learning attitudes and attributes from multi-aspect reviews,” in Proc. 2012 IEEE 12th International Conference on Data Mining (ICDM), Dec. 2012, 994 pp. 1020–1025. ↩

3. D. Antognini and B. Faltings, “HotelRec: A novel very large-scale hotel recommendation dataset,” in Proc. 12th Language Resources and Evaluation Conference (LREC), May 2020, pp. 4917–4923. ↩

4. OpenRice Hong Kong ↩