Book-Item-Based-Collaborative-Filtering
Collaborative filtering methods are used to determine a user’s level of interest in any product and to make recommendations by filtering products accordingly.
Product-based filtering, on the other hand, is a method that detects product similarities based on user votes. That is to say, for example, there are movies that show a similar liking structure with a movie that the person watches by being removed from being an object of the method. Similar movies can be found by finding similar reactions that other viewers collectively give to different movies. The movies with the highest correlation are selected and presented to the user as a recommendation.
Aim
Make suggestions based on product similarities
About this file
Books are identified by their respective ISBN. Invalid ISBNs have already been removed from the dataset. Moreover, some content-based information is given (Book-Title, Book-Author, Year-Of-Publication, Publisher), obtained from Amazon Web Services. Note that in case of several authors, only the first is provided. URLs linking to cover images are also given, appearing in three different flavours (Image-URL-S, Image-URL-M, Image-URL-L), i.e., small, medium, large. These URLs point to the Amazon web site.
There are 3 separate csvs named book rating and users.
Size of dataset : (1032345, 12)
https://www.kaggle.com/sebnemgurek/book-item-based-collaborative-filtering/data
