{
  "fetched_at": "2026-10-19T14:09:57Z",
  "source_url": "https://www.kaggle.com/competitions/fmi-su-recommender-system-hw-22022/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## Files\n\n*   **users.csv** - list with all users and their features which are at the train and/or test set\n*   **books.csv** - list with all books and their features which are at the train and/or test set\n*   **ratings.csv** - file which shows ratings given by the users to the books\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n",
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    {
      "name": "Description",
      "content": "This kaggle competition is part of the \"Recommender Systems\" course at Sofia University, FMI, 2022 year.\n\nThe domain of the competition is books recommedation as you have data about:\n\nUsers - id, location, age\nBooks - id, title, author, year, publisher\nand you have ratings from 0 to 10 which user id gave to book id\n\nUsers and Books files are the same for training and testing as\nfor training you have 862,335 ratings which the users gave to the books  and for testing you have 287,445 ratings which you need to predict.\n\nThe expectation is that you will try content based, collaborative filtering and/or hybrid approach as the evaluation will be with RMSE.\n",
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    {
      "name": "Evaluation",
      "content": "\nThe evaluation metric for this competition is RMSE\n\n## Submission Format\n\n**For every userid,bookid in the test_dataset**, submission files should contain 3 columns: `Id`,`Rating`. The ratings should be integer.\n\n\nThe file should contain a header and have the following format:\n```\n`Id`,`Rating`\n0,9\n1,10\n2,5\n3,10\n4,0\n```",
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    },
    {
      "name": "rules",
      "content": "You should work individually on the challenge.",
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