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I have a dataset containing records of books issued to students in college.

It comprises the following fields:

  • Department of student
  • Date on which book is issued to the student
  • Details of the book including title, author and publisher
  • Details of the student including name, batch, sex and department

I want to build a recommendation engine that recommends books to students based on their past activities. I don't know where to begin. Could you please suggest some resources to achieve this? This is a college project and I have no previous experience in this.

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You can use Collaborative Filtering.

Collaborative filtering (CF) and its modifications is one of the most commonly used recommendation algorithms. Even data scientist beginners can use it to build their personal movie recommender system, for example, for a resume project.

When we want to recommend something to a user, the most logical thing to do is to find people with similar interests, analyze their behavior, and recommend our user the same items. Or we can look at the items similar to ones which the user bought earlier, and recommend products which are like them.

These are two basic approaches in CF: user-based collaborative filtering and item-based collaborative filtering, respectively.

In both cases this recommendation engine has two steps:

1.Find out how many users/items in the database are similar to the given user/item. 2.Assess other users/items to predict what grade you would give the user of this product, given the total weight of the users/items that are more similar to this one.

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