{"id":16739603,"url":"https://github.com/praful932/kitabe","last_synced_at":"2025-04-06T11:09:01.119Z","repository":{"id":44951894,"uuid":"291985571","full_name":"Praful932/Kitabe","owner":"Praful932","description":"Book Recommendation System built for Book Lovers📖. 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[Objective](#objective-) ✍\n    - [Dataset](#dataset-) 🧾\n    - [PreProcessing](#preprocessing-) 🛠\n    - [Model Exploration](#model-exploration-) 🤯\n    - [Final Result](#final-result-) 😁\n- [Project Structure](#project-structure-%EF%B8%8F) 💁‍♀️\n- [To Do](#to-do-) 🎯\n- [Contribute](https://github.com/Praful932/Kitabe/blob/master/CONTRIBUTING.md) 🧏‍♂️\n- [Notebooks and Files](#notebooks-and-files-) 📓\n- [References](#references-) 😇\n- [Contributors](#contributors-) 🤗\n- [License](#license-) ✍\n\n### Demo 🎥\n\n![kitabe](https://user-images.githubusercontent.com/45713796/98460071-f6a23980-21c6-11eb-881f-ba0f75896751.gif)\u003cbr\u003e\n[Live Application](https://kitabe.up.railway.app/) 🌐\n\n### Objective ✍\nOur objective is to build an application for all Book Lovers ♥ like us out there where all you have to\ndo is rate some of your favorite books and the application will do it's **voodoo magic** 🧙‍♂️ and give you some more books that you may **love😍 to read**.\n\n### Dataset 🧾\nThe Dataset that we used for this task is the [goodbooks-10k](https://github.com/zygmuntz/goodbooks-10k) dataset. It consists of 10k books with a total of 6 million ratings. That's huge right! 😮. There are some more huge datasets such as [Book-Crossings](http://www2.informatik.uni-freiburg.de/~cziegler/BX/) but they are kinda old 😬.\n\n**Dataset Structure**\n```\nGoodBooks10k\n    ├── books.csv         # Contains book info with book-id\n    ├── ratings.csv       # Maps user-id to book-id and rating\n    ├── book_tags.csv     # Contains tag-id associated with book-ids\n    ├── tags.csv          # Contains tag-name associated with tag-id\n    ├── to_read.csv       # Contains book-ids marked as to-read by user\n```\n\n### PreProcessing 🛠\nSince this is a recommendation problem, we have to make sure that the `books.csv` is as clean as possible and only consider those ratings whose book-id is present, same goes for vice versa.\n\nMore Cleaning for `books.csv`\n- Missing Book Image URLs\n- Book \u0026 Rating Duplicates\n\n### Model Exploration 🤯\nFor Recommendation Problems there are multiple approaches that are possible:\n- Embedding Matrix\n- Singular Matrix Decomposition\n- Term Frequency\n\nWe experimented with several methods and chose Embedding Matrix \u0026 Term Frequency.\n\n- **Embedding Matrix** - This method is often called [FunkSVD](https://www.coursera.org/lecture/matrix-factorization/deriving-funksvd-lyTpD) which won the Netflix Prize back in 2004. Since it is a gradient based function minimization approach we like to call it as Embedding Matrix. Calling it SVD [confuses](https://www.quora.com/What-is-the-difference-between-SVD-and-matrix-factorization-in-context-of-recommendation-engine/answer/Luis-Argerich) it with the one in Linear Algebra. This Embedding Matrix constructs a vector for each user and each book, such that when the product is applied with additional constraints it gives us the rating. For more elaborate info on FunkSVD refer [this](http://sifter.org/~simon/journal/20061211.html).\nWe used the book embedding as a representation of the books to infer underlying patterns. This led to the embedding able to detect books from the same authors and also infer genres such as Fiction, Autobiography and more.\n\n- **Term Frequency** - This method is like a helper function to above, it shines where embedding fails. Term Frequency takes into account the tokens in a book title be it the book title itself, the name of authors and also rating. Taking into consideration it finds books which match closely with the tokens in the rated book.\n\n\u003e 🛠 Code for every step can be found in the [Notebooks and Files](#notebooks-and-files) Section.\n\n### Final Result 😁\nThe [Image](https://coggle.it/diagram/X6TOUxlMvSl8FBM4/t/dataset/7083ac4f2de39517a4d97cd9d3d211c11af6e65f9a0034c46d613ff0f9cd5) says it All.\n\n![coggle](https://user-images.githubusercontent.com/45713796/98331008-ae95e200-2021-11eb-915b-892854f88a6e.png)\n\n\n### Project Structure 💁‍♀️\n```\nKitabe\n│\n├───BookRecSystem               # Main Project Directory\n│\n├───mainapp                     # Project Main App Directory\n│   │\n│   └───migrations              # Migrations\n│\n├───static\n|   |                           # Static Directory\n│   └───mainapp\n│       ├───css                 # CSS Files\n|       |\n│       ├───dataset             # Dataset Files\n│       │\n│       ├───gif                 # GIF Media\n│       │\n│       ├───model_files         # Model Files\n|       |   |\n│       │   ├───surprise        # FunkSVD Files\n│       │   │\n│       │   └───cv              # CV Files\n│       │\n│       └───png                 # PNG Media FIles\n|\n└───templates                   # Root Template DIrectory\n    |\n    ├───account                 # Account App Templates\n    │\n    └───mainapp                 # Project Main App Templates\n\n```\n\n### To Do 🎯\n- [X] Display Popular Books Among Users\n- [X] Add AJAX View Tests\n- [X] Add Model Tests\n- [X] Use a Better Approach than Count Vectorizer\n\n### Notebooks and Files 📓\n- [All Dataset \u0026 Model Files](https://drive.google.com/drive/folders/1SvuCvfiSxwuF21EvmKyhSkuwjgK7KU6S?usp=sharing)\n- [Cleaning and Embedding Notebook](https://drive.google.com/file/d/1wlKiSvYQEXG7xtru5jDQWQwxffaVd9Ap/view?usp=sharing)\n- [Fix Missing Images Notebook](https://drive.google.com/file/d/1S0pd5t9oU9a63EdmlXmxhNWGc228W3ke/view?usp=sharing)\n- [Genre Wise \u0026 Tfidf Vectorizer Notebook](https://drive.google.com/file/d/1LRr4Nm2I2HRJUTXbRea3sK5A1Bvp_lav/view?usp=sharing)\n\n### References 😇\n\n- [Dataset](https://github.com/zygmuntz/goodbooks-10k)\n- [Count Vectorizer](https://www.kaggle.com/sasha18/recommend-books-using-count-tfidf-on-titles)\n- [Books2Rec](https://github.com/dorukkilitcioglu/books2rec)\n\n### Contributors 🤗\n![2](https://contributors-img.web.app/image?repo=Praful932/Kitabe)\n\n### License ✍\n```\nMIT License\n\nCopyright (c) 2020 Praful Mohanan \u0026 Prajakta Mane\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpraful932%2Fkitabe","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpraful932%2Fkitabe","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpraful932%2Fkitabe/lists"}