Machine Learning Based Recommendation Systems
INTRODUCTION
The basic purpose of a recommendation system is to find and recommend items that a user is most likely to be
interested in. When we visit e-commerce sites such as Amazon, Apple Music, or Netflix, your host recommends you some products. These recommendations are based on your past purchases or the products you might be interested in. The system or application behind calculating these endorsements is called recommendation system. It saves users’ time by giving them the best of their choice and increases the potential sale of the business.
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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:
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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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