What is a Recommendation system?
There are a lot of applications where websites collect data from their users and use that data to predict the likes and dislikes of their users. This allows them to recommend the content that they like. Recommender systems are a way of suggesting similar items and ideas to a user’s specific way of thinking.
There are basically two types of recommender Systems:
- Collaborative Filtering: Collaborative Filtering recommends items based on similarity measures between users and/or items. The basic assumption behind the algorithm is that users with similar interests have common preferences.
- Content-Based Recommendation: It is supervised machine learning used to induce a classifier to discriminate between interesting and uninteresting items for the user.
In this article, we will mainly focus on the Collaborative Filtering method.
Collaborative Filtering in Machine Learning
If this time you are watching a horror video on youtube then next time you will automatically see some more horror videos in your feed have you ever thought about how this thing works? Like how an application was able to get to know about your choices and likes. This is exactly what is popularly known as Recommendation Systems.
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