https://github.com/MengtingWan/goodreads

code samples for the goodreads datasets
https://github.com/MengtingWan/goodreads

book-reviews computational-social-science dataset machine-learning natural-language-processing recommendation-system recommender-system research spoilers

Last synced: 2 months ago
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code samples for the goodreads datasets

• Host: GitHub
• URL: https://github.com/MengtingWan/goodreads
• Owner: MengtingWan
• License: apache-2.0
• Created: 2019-05-23T06:17:08.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-05-29T22:12:34.000Z (over 1 year ago)
• Last Synced: 2024-08-03T18:14:57.847Z (6 months ago)
• Topics: book-reviews, computational-social-science, dataset, machine-learning, natural-language-processing, recommendation-system, recommender-system, research, spoilers
• Language: Jupyter Notebook
• Homepage: https://mengtingwan.github.io/data/goodreads.html
• Size: 74.2 KB
• Stars: 236
• Watchers: 3
• Forks: 58
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-libgen - Goodreads Metadata - Goodreads meta-data and book reviews on 2,360,655 books (eBook metadata)

README

        
# [Goodreads Datasets](https://mengtingwan.github.io/data/goodreads.html)

#### NOTE: Our datasets have been moved! Please see our new [webpage](https://mengtingwan.github.io/data/goodreads.html) about how to download these datasets.

The datasets were collected in late 2017 from [goodreads](https://goodreads.com). Details of the datasets are described in the [dataset website](https://mengtingwan.github.io/data/goodreads.html)

**We collected these datasets for academic use only! Please do not redistribute them or use for commercial purposes.**

## Citations

If you are using our dataset, please cite the following papers:

- Mengting Wan, Julian McAuley, "[Item Recommendation on Monotonic Behavior Chains](https://github.com/MengtingWan/mengtingwan.github.io/raw/master/paper/recsys18_mwan.pdf)", in RecSys'18. [[bibtex](https://dblp.uni-trier.de/rec/bibtex/conf/recsys/WanM18)]

- Mengting Wan, Rishabh Misra, Ndapa Nakashole, Julian McAuley, "[Fine-Grained Spoiler Detection from Large-Scale Review Corpora](https://github.com/MengtingWan/mengtingwan.github.io/raw/master/paper/acl19_mwan.pdf)", in ACL'19. [[bibtex](https://dblp.uni-trier.de/rec/bibtex/conf/acl/WanMNM19)]

## Notebooks/Code Samples

We've created several notebooks (in python 3.7) to illustrate how to download/read these datasets, and provide some basic explorations of the data.

- [download.ipynb](/download.ipynb): If you prefer to download datasets without GUI. This notebook will show how to download files in bash/python.

- [samples.ipynb](/samples.ipynb): This notebook will show how to read '.json.gz' files line-by-line and display sample records of each file.

- [statistics.ipynb](/statistics.ipynb): This notebook will calculate some basic statistics of the datasets (except the largest complete interaction file 'goodreads_interactions.csv'). Running this notebook may take a while.

- [distributions.ipynb](/distributions.ipynb): This notebook will operate on the complete interaction file 'goodreads_interactions.csv' and provide some explorations of the distributions of these interactions. **Note: Run this notebook only when you have LARGE memory (recommend 32g+)!!**

- [reviews.ipynb](/reviews.ipynb): This notebook will calculate some statistics of the review datasets.