This repository contains a book recommender system, using the data made available by Goodreads. Goodreads is an American social cataloging website that allows individuals to search freely its database of books, annotations, quotes, and reviews. Users can sign up and register books to generate library catalogs and reading lists. I have used Item-Based Collabrative filtering technique to built recommendation system.
https://www.kaggle.com/zygmunt/goodbooks-10k
The data used for this task consist of 5 different datasets which are as below :
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books.csv - Books contains all the information about the rated books, including author, title, book ID, publication year, average rating, etc.
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ratings.csv - Ratings includes all the rates given by our selected group of users to the books they have chosen to rate.
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book_tags.csv - Book Tags contains all the tags associated to each and every book included in the analysis.
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tags.csv - Tags collects all the tags included in 'bookTags' and explains their meaning.
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to_read.csv - to_read indicates all the books that have been flagged as 'to read' by the readers included in the analysis.
Jupyter Notebook