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RecSYS Book User

Data for recommendation systems user-item

@kaggle.aleksandrmogilevskiy_recsys_book_user

About this Dataset

RecSYS Book User

Hi!✋
This is not my dataset. I found it on the website towardsdatascience.com. It's data help me learn to work with recommendation systems.
Data🔥 :

  1. books_poetry.csv - data about items and their features
  2. interactions.csv - data about interactions users-items

Good luck!)🖤

Tables

Books Poetry

@kaggle.aleksandrmogilevskiy_recsys_book_user.books_poetry
  • 37.94 MB
  • 36514 rows
  • 30 columns
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CREATE TABLE books_poetry (
  "unnamed_0" BIGINT,
  "isbn" VARCHAR,
  "text_reviews_count" BIGINT,
  "series" VARCHAR,
  "country_code" VARCHAR,
  "language_code" VARCHAR,
  "popular_shelves" VARCHAR,
  "asin" VARCHAR,
  "is_ebook" BOOLEAN,
  "average_rating" DOUBLE,
  "kindle_asin" VARCHAR,
  "similar_books" VARCHAR,
  "description" VARCHAR,
  "format" VARCHAR,
  "link" VARCHAR,
  "authors" VARCHAR,
  "publisher" VARCHAR,
  "num_pages" DOUBLE,
  "publication_day" DOUBLE,
  "isbn13" VARCHAR,
  "publication_month" DOUBLE,
  "edition_information" VARCHAR,
  "publication_year" DOUBLE,
  "url" VARCHAR,
  "image_url" VARCHAR,
  "book_id" BIGINT,
  "ratings_count" BIGINT,
  "work_id" BIGINT,
  "title" VARCHAR,
  "title_without_series" VARCHAR
);

Interactions

@kaggle.aleksandrmogilevskiy_recsys_book_user.interactions
  • 19.63 MB
  • 250000 rows
  • 11 columns
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CREATE TABLE interactions (
  "unnamed_0" BIGINT,
  "user_id" VARCHAR,
  "book_id" BIGINT,
  "review_id" VARCHAR,
  "is_read" BOOLEAN,
  "rating" BIGINT,
  "review_text_incomplete" VARCHAR,
  "date_added" VARCHAR,
  "date_updated" VARCHAR,
  "read_at" VARCHAR,
  "started_at" VARCHAR
);