Recommendation systems are becoming increasingly important in today’s hectic world. People are always in the lookout for products/services that are best suited for them. Therefore, the recommendation systems are important as they help them make the right choices, without having to expend their cognitive resources.

In this blog, we will understand the basics of Recommendation Systems and learn how to build a Movie Recommendation System using collaborative filtering by implementing the K-Nearest Neighbors algorithm. We will also predict the rating of the given movie based on its neighbors and compare it with the actual rating.

Types of Recommendation Systems

Recommendation systems can be broadly classified into 3 types —

1. Collaborative Filtering
2. Content-Based Filtering
3. Hybrid Recommendation Systems

Collaborative Filtering

This filtering method is usually based on collecting and analyzing information on user’s behaviors, their activities or preferences, and predicting what they will like based on the similarity with other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and thus it is capable of accurately recommending complex items such as movies without requiring an “understanding” of the item itself.

Further, there are several types of collaborative filtering algorithms —

• User-User Collaborative Filtering: Try to search for lookalike customers and offer products based on what his/her lookalike has chosen.
• Item-Item Collaborative Filtering: It is very similar to the previous algorithm, but instead of finding a customer lookalike, we try finding item lookalike. Once we have item lookalike matrix, we can easily recommend alike items to a customer who has purchased an item from the store.
• Other algorithms: There are other approaches like market basket analysis, which works by looking for combinations of items that occur together frequently in transactions.

Collaborative v/s Content-based filtering illustration

#collaborative-filtering #machine-learning #knn-algorithm #recommendation-system #movies #deep learning