mushdog/thedatasetsdilemma

Code for our WSDM 2022 paper titled "The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?"

Code for the WSDM 2022 paper

The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?

Hello! :)

This repository contains the source code, as well as other useful information, for the paper "The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?" in WSDM 2022.

The paper is available here: Paper (Best Paper Award Runner-up)

For a quick overview of the paper, you can refer to these slides: The Datasets Dilemma Slides

Reference

Please consider citing our work if you find it useful, thank you!

@inproceedings{10.1145/3488560.3498519,
  author = {Chin, Jin Yao and Chen, Yile and Cong, Gao},
  title = {The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?},
  year = {2022},
  isbn = {9781450391320},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3488560.3498519},
  doi = {10.1145/3488560.3498519},
  booktitle = {Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining},
  pages = {141–149},
  numpages = {9},
  keywords = {datasets, item recommendation, evaluation, data characteristics},
  location = {Virtual Event, AZ, USA},
  series = {WSDM '22}
}

Outline

In our paper, we try to address the "datasets dilemma" using 3 main steps.

1. How are different datasets being utilised in recent papers?
   • Are there any patterns?
   • Code can be found in the ./Step 1/ folder (Please refer to its README file)
2. What are the similarities as well as differences between various datasets?
   • Can we define them using objective measures?
   • Code can be found in the ./Step 2/ folder (Please refer to its README file)
3. If the choice of datasets used could influence the observations and/or conclusions obtained
   • Empirical study using a variety of item recommendation algorithms
   • Code can be found in the ./Step 3/ folder (Please refer to its README file)

The ./Datasets/ folder

• ./Datasets/Source/ contains the raw datasets
• ./Datasets/Preprocessed/ contains the preprocessed datasets
• The dataset characteristics (as well as other information) for all 51 datasets: characteristics_all.txt
  • Basic Dataset Information (in a table format): characteristics_table_basic_detailed.txt
  • Dataset Characteristics (in a table format): characteristics_table_basic_advanced.txt

Environment Setup

1. Python 3.6.8
2. PyTorch 1.4.0
3. Tensorflow 2.3.0
4. numpy 1.17.2
5. pandas 0.25.3
6. matplotlib 3.3.2
7. scikit-learn 0.23.2
8. scipy 1.3.0
9. scikit-optimize 0.8.1
10. mlxtend 0.18.0 (for frequent itemset mining)
11. implicit 0.4.4 (for Weighted Matrix Factorization (WMF))

Analyses & experiments were conducted on a Ubuntu server with version 16.04.6 LTS, and conda 4.8.4.