Machine Learning Based Recommendation Systems

1. INTRODUCTION



Formally, we define a recommendation system as:

The Recommendation System is a computer program that filters and recommends product or content to users by analyzing their behavior of rating or preference they had given in the past.

Examples:

• Recommendation of Movies and shows by Netflix.
• Recommendation of music by Apple music store.
• Social connection recommendations by Facebook, LinkedIn, or Instagram.
• Recommendation of dates by dating applications.
• Banking and insurance products recommendation.


2. TYPES

Collaborative Filtering: Use knowledge of user’s past purchase/selection or similar decisions by other users to recommend products (User-based recommendation).

Advantage – Product knowledge not required.

Disadvantage – Can’t recommend products if no user reviews available so difficult to make recommendations for new users. It may be biased towards products with high reviews than the product with low reviews.


Content-Based Recommender: Use knowledge of each product to recommend a similar product (Product based recommendation).

Advantage – Even works without user reviews.

Disadvantage – Need descriptive data for every product so difficult to implement for large inventory products.

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