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      "content": "This dataset contains more than 1.3M book reviews about 25,475 books and 18,892 users , which is a review subset for spoiler detection, where each book/user has at least one associated spoiler review.\n\n## Files\n*   **goodreads_train.csv** - the training set\n*   **goodreads_test.csv** - the test set\n*   **goodreads_sample_submission.csv** - sample submission file \n\nHere are the columns of the dataset - \n\nuser_id - Id of user \n\nbook_id - Id of Book \n\nreview_id - Id of review\n \nrating - rating from 0 to 5 \n\nreview_text - review text\n\ndate_added - date added \n\ndate_updated - date updated\n\nread_at - read at \n\nstarted_at - started at \n\nn_votes - no. of votes\n\nn_comments - no. of comments",
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      "name": "Description",
      "content": "# Goodreads Books Review Rating Prediction\n\nReviews are a good way to judge the quality of any product, whether it's books, clothes, technology, or anything else. When you want to buy something online these days, the first thing that comes to mind is the reviews from past buyers and the overall rating the product has received.\nReader feedback, whether positive or negative, five stars or one star, will encourage the product owner to make improvements.\nReader connection and engagement will be encouraged by book reviews, whether they be left on Amazon, Goodreads, or social media. Readers must determine whether or not other readers are enjoying the book.\n\nIn this competition you will work with a challenging dataset consisting reviews from the [Goodreads](https://www.goodreads.com/) book review website, and a variety of attributes describing the items. and you have to predict review rating which ranges from 0 to 5.\n\n# Acknowledgements\nThe dataset of this competition is taken from [UCSD Book Graph](https://sites.google.com/corp/eng.ucsd.edu/ucsdbookgraph/reviews?authuser=0).\nIf you have any questions or find any bugs regarding these datasets, feel free to contact Mengting Wan (m5wan@ucsd.edu). \n\n## Citations\nplease cite the following papers:\n* Mengting Wan, Julian McAuley, \"[Item Recommendation on Monotonic Behavior Chains](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMengtingWan%2Fmengtingwan.github.io%2Fraw%2Fmaster%2Fpaper%2Frecsys18_mwan.pdf&sa=D&sntz=1&usg=AFQjCNGGcNRW1tSZKPWO0yZsr8mj7MkWuw)\", in RecSys'18. [[bibtex](https://www.google.com/url?q=https%3A%2F%2Fdblp.uni-trier.de%2Frec%2Fbibtex%2Fconf%2Frecsys%2FWanM18&sa=D&sntz=1&usg=AFQjCNEy2HDVC1K59JJWybzGgq3MafQcWQ)]\n* 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)]\n ",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [**Mean F1-Score**](https://en.wikipedia.org/wiki/F-score). The **F1 score**, commonly measures accuracy using the precision(p) and recall(r).\n\nPrecision is the ratio of true positives(tp) to all predicted positives (tp + fp). Recall is the ratio of true positives (tp) to all actual positives (tp + fn). \n\nThe F1 score is given by:\n$$ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} $$\n\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.\n\n## Submission Format\n\nsubmission file should contain a header and have the following format:\n```\nreview_id,rating\n5c4df7e70e9b438c761f07a4620ccb7c,0\n8eaeaf13213eeb16ad879a2a2591bbe5,5\ndce649b733c153ba5363a0413cac988f,4\n8a46df0bb997269d6834f9437a4b0a77,2\n```",
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    {
      "name": "rules",
      "content": "# One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n# No private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n# Team Mergers\nTeam mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.\n\n# Team Limits\nThree individuals.\n\n# Submission Limits\nYou may submit a maximum of 5 entries per day.\n",
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      "name": "Timeline",
      "content": "**Submission Deadline for Overall Competition Leaderboard:** \n - 04/01/2023 11:59 PM UTC\n  - Overall competition leaderboard prize: kudos only\n\n**Deadline to make your notebook public in order to be considered for the \"Kaggle Swag\" Prize:** \n - 08/01/2022 11:59 PM UTC\n- \"Kaggle Swag\" Prize: A Kaggle T-Shirt given as a gift to the author of our favorite public notebook that makes use of Google's open source ML software ecosystem (e.g. TensorFlow, JAX, etc) ",
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      "name": "Prizes",
      "content": "**Overall competition leaderboard prize:**\n - Kudos only\n - Submission deadline: 04/01/2023 11:59 PM UTC\n\n**\"Kaggle Swag\" Prize:**\n - A Kaggle T-Shirt given as a gift to the author of our favorite public notebook that makes use of Google's open source ML software ecosystem (e.g. TensorFlow, JAX, etc) \n - Deadline by which notebook needs to be made public: 08/01/2022 11:59 PM UTC",
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