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README.md

Reviews4Rec

This repository contains many popular recommender algorithms which use reviews as additional data. The code accompanies the paper "How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements" [ACM] [Public PDF] where we critique different existing review-based recommendation algorithms and questions their reported performance.

If you find any module of this repository helpful for your own research, please consider citing the below SIGIR'20 paper. Thanks!

@inproceedings{SachdevaMcAuley20,
  author = {Noveen Sachdeva and Julian McAuley},
  title = {How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements},
  booktitle = {ACM Conference on Research and Development in Information Retrieval (SIGIR)},
  year = {2020}
}

Code Author: Noveen Sachdeva (ernoveen@gmail.com)


Environents


Setup

Data Setup

Once you've correctly setup the python environments and downloaded the dataset of your choice (Amazon: http://jmcauley.ucsd.edu/data/amazon/), the following steps need to be run:

$ ./prep_all_data.sh <HUMAN_FRIENDLY_DATASET_NAME> path/to/data/file.json

The above command will create the train/test/val splits along with some pre-processing scripts for running review-based methods like DeepCoNN/NARRE/TransNet much faster.

Setup for MPCN (Skip if not needed)

Since running MPCN requires a Python2.7 environment, you will need to modify Line#2 in the script run_MPCN_in_p2.sh to edit how to switch to the Python2.7 environment.


Run Instructions

  • Edit the hyper_params.py file which lists all config parameters, including what type of model to run. Currently supported models:
Model Type Model Name Paper Link
Non-textual bias_only (or) baseline
Non-textual MF_dot (or) NMF
Non-textual SVD (or) SVD++
Non-textual MF
Non-textual NeuMF LINK
Reviews as regularizer HFT LINK
Reviews as features deepconn (or) deepconn++ LINK
Reviews as features NARRE LINK
Reviews as features transnet (or) transnet++ LINK
Reviews as features MPCN LINK
  • Finally, type the following command to run:
$ CUDA_VISIBLE_DEVICES=<SOME_GPU_ID> python main.py

Contribution

As more and more algorithms using reviews for recommendation are published, please feel free to send a pull request with your algorithm and I'll be happy to merge it into this repository.

License


MIT

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[ SIGIR '20 ] How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements

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