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Collaborative Filtering Recommender Systems - Item Based approach


In the series of implementing Recommendation engines, in my previous blog about recommendation system in R, I have explained about implementing user based collaborative filtering approach using R. In this post, I will be explaining about basic implementation of Item based collaborative filtering recommender systems in r. 

Intuition:


Item based Collaborative Filtering:
Unlike in user based collaborative filtering discussed previously, in item-based collaborative filtering, we consider set of items rated by the user and computes item similarities with the targeted item. Once similar items are found, and then rating for the new item is predicted by taking weighted average of the user’s rating on these similar items.

let's understand with an example: 
As an example: consider below dataset, containing users rating to movies. Let us build an algorithm to recommend movies to CHAN.
Implementing Item based recommender systems, like user based collaborative filtering, requires two steps: 
  • Calculating Item similarities
  •  Predicting the targeted item rating for the targeted User.
Code Implementation can be found here

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