jtarakram / book_recommendation_system_collaborative_based

Book_recommendation

The practise of offering or recommending commodities, products, or services to people based on their interests or preferences is referred to as recommendation. To assist consumers in discovering relevant and entertaining material, recommendation systems are extensively utilised in e-commerce, entertainment, social media, and many other sectors.
There are several types of recommendation systems, but the following are the most common:

• Collaborative filtering.
• Content-based filtering.
• Hybrid recommendation.
  In this project, we mainly focused on Collaborative filtering
  Collaborative filtering is a form of recommendation system in which users collaborate or work together to locate items of interest. In other words, it is a strategy for predicting a user's preferences based on the preferences of other users who have similar interests to them.
  

The system takes data on prior user activity, such as things purchased, reviewed, or seen, and utilises this information to offer suggestions for similar items that the user may also like. The theory is that if two customers had similar preferences in the past, they will most likely have similar choices in the future.
Collaborative filtering is classified into two types:

• user-based and item-based. User-based collaborative filtering seeks for other users who share the user's interests and recommends items that they have enjoyed.
• Item-based collaborative filtering, on the other hand, seeks for and suggests goods that are comparable to those that the user has previously enjoyed. DATA - There are three datasets.
  Understanding the Datset - 1
  
• ISBN - International Standard Book Number, is a unique number that is assigned to every published book
  
• Book-title - Title of the book
  
• Book_Author - Author of the book
  
• Year-of-Publication - In which the particular book had published.
  
• publisher - by which company the book has been publoshsed.
  
• Image-URL-S - image url (small)
  
• Imgae-URL-M - image url (Medium)
  
• Imgae-URL-L - image url (Large)
  Understanding the Dataset -2
  
• User-ID - the Id of the particular Reader
  
• ISBN - International Standard Book Number, is a unique number that is assigned to every published book
  
• Book-rating - Rating of particular book that has been ratede by the user.
  Understand the Dataset - 3
  
• User-ID - Id of the particular user.
  
• Location - the location of the user(reader of the book)
  
• Age - Age of the user (reader of the book)