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Goodreads Datasets

Goodreads Datasets

Stefan
Open Dataset
Creators: Wan, Mengting; McAuley, Julian
Publication Date: 2017
Creators: Wan, Mengting; McAuley, Julian

The Goodreads Datasets provide a large-scale collection of book-related data, making them valuable for analyzing reading behavior, book popularity, and recommendation systems. They contain rich metadata on over 2.3 million books, including titles, authors, publication years, genres, and average ratings. Additionally, they feature nearly 229 million user-book interactions, capturing explicit preferences such as bookshelf assignments (“read,” “to-read”) and user ratings. The dataset also covers a vast collection of user-generated textual reviews, offering insights into reader sentiments and opinions. The Goodreads Datasets contain three primary components: book metadata, user-book interactions, and book reviews. The book metadata includes details on 2,360,655 books, such as title, author, publication date, and genre. The user-book interactions dataset comprises 228,648,342 interactions from 876,145 users, capturing activities like adding books to shelves (“read,” “to-read”) and providing ratings. The book reviews dataset contains detailed textual reviews written by users, providing insights into reader sentiments and book popularity.The total size of the combined datasets is approximately 11 GB, available in JSON and CSV formats.

Trip Advisor Hotel Reviews

Trip Advisor Hotel Reviews

Stefan
Open Dataset
Creators: Alam, Md. H.; Ryu, Woo-Jong; Lee, SangKeun
Publication Date: 2020
Creators: Alam, Md. H.; Ryu, Woo-Jong; Lee, SangKeun

Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged. With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels. It enables the exploration of factors contributing to hotel quality and can be utilized for sentiment analysis and natural language processing tasks. The dataset is 0,01 GB in size and covers textual reviews of hotels in combination with a numerical rating associated with the reviews.

Flickr30k

Flickr30k

Stefan
Open Dataset
Creators: Young, Peter; Lai, Alice; Hodosh, Micah; Hockenmaier, Julia
Publication Date: 2014
Creators: Young, Peter; Lai, Alice; Hodosh, Micah; Hockenmaier, Julia
The Flickr30k dataset consists of 31,783 images, each accompanied by five human-generated captions, adding up to 158,915 captions. These images predominantly depict people engaged in everyday activities and events. The dataset serves as a benchmark for sentence-based image description tasks. Each image is associated with five descriptive captions provided by human annotators. The dataset has been further enhanced by the Flickr30k Entities extension, which adds 244,000 coreference chains linking mentions of the same entities across different captions for the same image, and associates them with 276,000 manually annotated bounding boxes. This augmentation facilitates tasks such as phrase localization and grounded language understanding.

ImageNet Large Scale Visual Recognition Challenge

ImageNet Large Scale Visual Recognition Challenge

Stefan
Open Dataset
Creators: Russakovsky, Olga; Deng, Jia; Su, Hao; Krause, Jonathan; Satheesh, Sanjeev; Ma, Sean; Huang, Zhiheng; Karpathy, Andrej; Khosla, Aditya; Bernstein, Michael; Berg, Alexander C.; Fei-Fei, Li
Publication Date: 2009
Creators: Russakovsky, Olga; Deng, Jia; Su, Hao; Krause, Jonathan; Satheesh, Sanjeev; Ma, Sean; Huang, Zhiheng; Karpathy, Andrej; Khosla, Aditya; Bernstein, Michael; Berg, Alexander C.; Fei-Fei, Li

ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. The project has been instrumental in advancing computer vision and deep learning research. It contains data from 2012 until 2017. The dataset includes over 14 million images, while the biggest subset ImageNet Large Scale Visual Recognition Challenge (ILSVRC) covers 1,281,167 training images, 50,000 validation images, and 100,000 test images. In total, the dataset has a size of 167 GB. The data is available for free to researchers for non-commercial use on the data provider’s website.

For access to the full ImageNet dataset and other commonly used subsets, please login or request access on the website of the data providers. In doing so, you will need to agree to the ImageNet’s terms of access. Therefore, no data preview can be provided here.

When reporting results of the challenges or using the datasets, please cite:

Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015.

File Descriptions

1) ILSVRC/ contains the image data and ground truth for the train and validation sets, and the image data for the test set.

  • The image annotations are saved in XML files in PASCAL VOC format. Users can parse the annotations using the PASCAL Development Toolkit.
  • Annotations are ordered by their synsets (for example, “Persian cat”, “mountain bike”, or “hot dog”) as their wnid. These id’s look like n00141669. Each image’s name has direct correspondence with the annotation file name. For example, the bounding box for n02123394/n02123394_28.xml is n02123394_28.JPEG.
  • You can download all the bounding boxes of a particular synset from http://www.image-net.org/api/download/imagenet.bbox.synset?wnid=%5Bwnid]
  • The training images are under the folders with the names of their synsets. The validation images are all in the same folder. The test images are also all in the same folder.
  • ImageSet folder contains text files specifying lists of images for the main localization task.

2) LOC_sample_submission.csv is the correct format of the submission file. It contains two columns:

  • ImageId: the id of the test image, for example ILSVRC2012_test_00000001
  • PredictionString: the prediction string should be a space delimited of 5 integers. For example, 1000 240 170 260 240 means it’s label 1000, with a bounding box of coordinates (x_min, y_min, x_max, y_max). We accept up to 5 predictions. For example, if you submit 862 42 24 170 186 862 292 28 430 198 862 168 24 292 190 862 299 238 443 374 862 160 195 294 357 862 3 214 135 356 which contains 6 bounding boxes, we will only take the first 5 into consideration.

3) LOC_train_solution.csv and LOC_val_solution.csv: These information are available in ILSVRC/ already, but we are providing them in csv format to be consistent with LOC_sample_submission.csv. Each file contains two columns:

  • ImageId: the id of the train/val image, for example n02017213_7894 or ILSVRC2012_val_00048981
  • PredictionString: the prediction string is a space delimited of 5 integers. For example, n01978287 240 170 260 240 means it’s label n01978287, with a bounding box of coordinates (x_min, y_min, x_max, y_max). Repeated bounding boxes represent multiple boxes in the same image: n04447861 248 177 417 332 n04447861 171 156 251 175 n04447861 24 133 115 254

4) LOC_synset_mapping.txt: The mapping between the 1000 synset id and their descriptions. For example, Line 1 says n01440764 tench, Tinca tinca means this is class 1, has a synset id of n01440764, and it contains the fish tench.

Google Local Reviews

Google Local Reviews

Stefan
Open Dataset
Creators: He, Ruining; Kang, Wang-Cheng; McAuley, Julian
Publication Date: 2017
Creators: He, Ruining; Kang, Wang-Cheng; McAuley, Julian

The Google Local Reviews dataset comprises 11,453,845 reviews and ratings from 4,567,431 users on 3,116,785 local businesses. Each business entry dataset contains user reviews for local businesses, including variables such as rating, review text, business category, location details (address, GPS, phone number, opening hours), and user and business IDs. It also includes timestamps of reviews, price level, and whether the business is closed. The dataset has a size of 7 GB and spans 48,013 categories of local businesses across five continents, encompassing a diverse range of establishments from restaurants and hotels to parks and shopping malls.

 

Marketing Bias data

Marketing Bias data

Stefan
Open Dataset
Creators: Wan, Mengting; Ni, Jianmo; Misra, Rishabh; McAuley, Julian
Publication Date: 2020
Creators: Wan, Mengting; Ni, Jianmo; Misra, Rishabh; McAuley, Julian

This dataset contains attributes of products sold on ModCloth and Amazon (in particular, attributes about how the products are marketed), which may introduce biases in recommendation systems. It is designed to facilitate research on marketing biases in product recommendations. Data also includes user/item interactions for recommendation.

The dataset amounts to 0,09 GB in size and is built upon two processed subsets:

  • ModCloth Dataset: Contains product attributes from the ModCloth platform.
  • Electronics Dataset: Comprises product attributes from Amazon’s Electronics category.

In total, the dataset includes 99,893 reviews for ModCloth and 1,292,954 reviews for the Electronics category of Amazon.

Amazon Product Reviews

Amazon Product Reviews

Stefan
Open Dataset
Creators: Ni, Jianmo; Li, Jiacheng; McAuley, Julian
Publication Date: 2018
Creators: Ni, Jianmo; Li, Jiacheng; McAuley, Julian

The Amazon Product Reviews dataset encompasses a comprehensive collection of 233.1 million customer reviews from Amazon, covering the period from May 1996 to October 2018. It includes various features such as ratings, textual reviews, helpfulness votes, product metadata (descriptions, category information, price, brand, and image features), and links to related products (e.g., also viewed, also bought graphs). The dataset serves as a valuable resource for analyzing consumer behavior, product trends, and for developing recommendation systems. In total, the dataset is 34 GB in size. It is organized into the following files and subsets:

  • Complete Review Data: A comprehensive file containing all 233.1 million reviews.
  • Ratings Only: A CSV file focusing solely on ratings, excluding textual reviews and metadata.
  • 5-Core: A subset where all users and items have at least five reviews, comprising 75.26 million reviews.
  • Per-Category Data: Reviews and product metadata categorized by specific product types (e.g., Books, Electronics).
Food.com Recipe & Review Data

Food.com Recipe & Review Data

Stefan
Open Dataset
Creators: Majumder, Bodhisattwa P.; Li, Shuyang; Ni, Jianmo; McAuley, Julian
Publication Date: 2019
Creators: Majumder, Bodhisattwa P.; Li, Shuyang; Ni, Jianmo; McAuley, Julian
This dataset consists of 180K+ recipes and 700K+ recipe reviews covering 18 years of user interactions and uploads on Food.com (formerly GeniusKitchen), an online recipe aggregator. This extensive collection allows for in-depth analysis of culinary trends, user preferences, and recipe characteristics over nearly two decades.The dataset is 0,85 GB in size and contains three sets of data from Food.com:Interaction splits

  • interactions_test.csv
  • interactions_validation.csv
  • interactions_train.csv

Preprocessed data for result reproduction

In this format, the recipe text metadata is tokenized via the GPT subword tokenizer with start-of-step, etc. tokens.

  • PP_recipes.csv
  • PP_users.csv

To convert these files into the pickle format required to run our code off-the-shelf, you may use pandas.read_csv and pandas.to_pickle to convert the CSV’s into the proper pickle format.

 

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