ratings

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goodbooks-10k

goodbooks-10k

Stefan
Open Dataset
Publication Date: 2017
Creators: Zając, Zygmunt

The dataset contains six million ratings for ten thousand most popular books (with most ratings). There are also books marked to read by the users, book metadata (author, year, etc.) and tags/shelves/genres.

ratings contains ratings sorted by time. Ratings go from one to five. Both book IDs and user IDs are contiguous. For books, they are 1-10000, for users, 1-53424.

to_read  provides IDs of the books marked “to read” by each user, as user_id,book_id pairs, sorted by time. There are close to a million pairs.

books has metadata for each book (goodreads IDs, authors, title, average rating, etc.). The metadata have been extracted from goodreads XML files.

book_tags contains tags/shelves/genres assigned by users to books. Tags in this file are represented by their IDs. They are sorted by goodreads_book_id  ascending and count descending.

The date set is 68.8 MB large.

TripAdvisor European restaurants

TripAdvisor European restaurants

Stefan
Open Dataset
Publication Date: 2021
Creators: (Leone, Stefano)

TripAdvisor is the most popular travel website and it stores data for almost all restaurants, showing locations (even latitude and longitude coordinates), restaurant descriptions, user ratings and reviews, and many more aspects.The dataset is 0.68 GB large.

The TripAdvisor dataset includes 1,083,397 restaurants with attributes such as location data, average rating, number of reviews, open hours, cuisine types, awards, etc.

The dataset combines the restaurants from the main European countries, the data has been scraped in early May 2021.

IMDb movies extensive dataset

IMDb movies extensive dataset

Stefan
Open Dataset
Publication Date: 2019
Creators: (Leone, Stefano)

The movies dataset includes 85,855 movies with attributes such as movie description, average rating, number of votes, genre, etc. The ratings dataset includes 85,855 rating details from demographic perspective. The names dataset includes 297,705 cast members with personal attributes such as birth details, death details, height, spouses, children, etc. The title principals dataset includes 835,513 cast members roles in movies with attributes such as IMDb title id, IMDb name id, order of importance in the movie, role, and characters played.

Goodreads Datasets

Goodreads Datasets

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

We collected three groups of datasets: (1) meta-data of the books, (2) user-book interactions (users’ public shelves) and (3) users’ detailed book reviews. These datasets can be merged together by matching book/user/review ids. The complete book graph includes 2,360,655 books, 876,145 users and 228,648,342 user-book interactions in users’ shelves.

Google Local Reviews

Google Local Reviews

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

We introduce a new dataset from Google which contains 11,453,845 reviews and ratings from 4,567,431 users on 3,116,785 local businesses (with detailed name, hours, phone number, address, GPS, etc.). Œere are as many as 48,013 categories of local businesses distributed over €ve continents, ranging from restaurants, hotels, parks, shopping malls, movie theaters, schools, military recruiting oces, bird control, mediation services (etc.)

Food.com Recipe & Review Data

Food.com Recipe & Review Data

Stefan
Open Dataset
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 dataset 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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