Recommend books using various machine learning algorithms.
During the last few decades, with the rise of Youtube, Amazon, Netflix, and many other such web services, recommender systems have taken more and more place in our lives.
In a very general way, recommender systems are algorithms aimed at suggesting relevant items to users (items being movies to watch, text to read, products to buy, or anything else depending on industries).
The Book-Crossing dataset comprises 3 files.
Contains the users. Note that user IDs ( ) have been anonymized and map to integers. Demographic data is provided ( , Age) if available. Otherwise, these fields contain NULL values.
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 the case of several authors, only the first is provided. URLs linking to cover images are also given, appearing in three different flavors (Image-URL-S, Image-URL-M, Image-URL-L), i.e., small, medium, large. These URLs point to the Amazon website.
Contains the book rating information. Ratings (Book-Rating) are either explicit, expressed on a scale from 1-10 (higher values denoting higher appreciation), or implicit, expressed by 0.
We tried to make models from various perspective like
Books are arranged in such a way that the books with the highest sum total of ratings are at the top.
Books are arranged in such a way that books that have been rated the most number of times irrespective of the rating are at the top.
Find similar items based on pearson correlation and took weighted average of ratings.
We used Scikit Surprise library for this model based approach. Surprise is an open-source Python library that makes it easy for developers to build recommender systems with explicit rating data.