Hello!

This is the implementation of our paper "MRRRec: Multi-criteria Rating and Review based Recommendation Model" in IEEE bigdata 2022.

The paper is available here: Paper

Environment Setup

1. Python 3.11.5
2. PyTorch 2.1.2+cud121

To run the code, please follow the steps below.

Step 0. We assume the following directory structure:

NAME your root directory # root folder

GoogleNews-vectors-negative300.bin # This is the file for pretrained word embeddings

MRRRec/ # Basically, what you clone from github..

__saved_models__/                   # This is where the pretrained MRRRec weights go to..

datasets/                           # Place the tripadvisor csv file here
  clean_tripdata/             

experimental_results/               # This is where your results go to..

model/                              # All model-related code (i.e. all the PyTorch stuff)

preprocessing/                      # All preprocessing code 

FILEPATHS.py                        # Names of files shared across all code
PyTorchTEST.py                      # Basically main.py.. The model is trained and tested here (despite the weird filename)

Step 1. Preprocessing

• NOTE: For this step, your current directory should be the 'preprocessing' folder..

• E.g. MRRRec/preprocessing/ in the example directory structure! run the following two commands

  a. python preprocessing_simple.py -d clean_tripdata -dev_test_in_train 1 b. python pretrained_vectors_simple.py -d clean_tripdata

Step 2. Running the model Come back to the folder MRRRec and run the following command

python PyTorchTEST.py -d "clean_tripdata" -m "MRRRec" -e 25 -p 1 -rs 1337 -gpu 0 -vb 1

• model output (some information & results) are saved to the 'experimental_results' folder
• e.g. MRRRec/experimental_results/clean_tripdata - MRRRec/2022-12-05-21-28-46-logs.txt

##########Tripadvisor dataset##############

dataset is available here: dataset

Follow the instructions in the paper to clean the dataset.

This is the implementation of our paper MRRRec2022 IEEE big data

##################################################################### Please note that our paper is the extension of ANR. Therefore, we added code to their implementation Github. The architecture that we extended, we DEVELOP THE CODE FOR THAT PART ONLY. The detailed architecture of our model can be found in the paper. Thank you very much to the author of ANR for their nice work and open-source work!

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Please consider citing our work if you find it useful. Thanks! ''' @inproceedings{hasan2022multi, title={Multi-criteria Rating and Review based Recommendation Model}, author={Hasan, Emrul and Ding, Chen and Cuzzocrea, Alfredo}, booktitle={2022 IEEE International Conference on Big Data (Big Data)}, pages={5494--5503}, year={2022}, organization={IEEE} } '''