In our day to day life, we come across a large number of Recommendation engines like Facebook Recommendation Engine for Friends’ suggestions, and suggestions of similar Like Pages, Youtube recommendation engine suggesting videos similar to our previous searches/preferences. In today’s blog post I will explain how to build a basic recommender System.
Types of Collaborative Filtering:
User based Collaborative Filtering
Item based Collaborative filtering
In this post will explain about User based Collaborative Filtering. This algorithm usually works by searching a large group of people and finding a smaller set with tastes similar to yours. It looks at other things they like and combines them to create a ranked list of suggestions.
Implementing User Based Collaborative Filtering:
This involves two steps:
Calculating Similarity Function
Recommend items to users based on user Similarity Score
Consider the below data sample of Movie critics and their movie rankings, the objective is to recommend the unrated movies based on similar users:
Step1- Calculate Similarity Score for CHAN:
Creating Similarity score for people helps us to identify similar people. We use Cosine based Similarity function to calculate the similarity between the users. Know more about cosine similarity here. In R we have a cosine function readily available:
user_sim = cosine(as.matrix(t(x)))
