product recommendation engine

A recommendation engine involves connecting users to products/ items.

Lets take an example. Let’s assume we have ratings for products . In an e-commerces store these might be implicit signals such as view, add to cart, buy , remove from cart, time spent on a product and much more.

For simplicity we will take ratings on a scale of (1-5). C1,C2.. are customers and P1, P2 .. are products. C1 has rated P1 4, C2 has rated P2 5 etc . The recommendation engine’s goal is to predict what would customer 3’s rating would be for P2 using the given ratings(or any other blank cell in the matrix).

P1P2P3P4
C1415
C253
C33?
C423
    • Recommendations are classified according to how these blanks in this matrix are filled.
  • Content based- using properties of items.
  • Collaborative filtering (CF)- using similarity of items and/or similarity of users. The items recommended to the user.
    • Neighborhood based approach
    • Model based approach.
    • Hybrid models.
  • Hybrid – CF + content based approach.

Hope this gives a simple introduction into what recommender systems are all about, I will discuss in detail more of these methods in my future posts.

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muthali ganesh

Muthali loves writing about emerging technologies and easy solutions for complex tech issues. You can reach out to him through chat or by raising a support ticket on the left hand side of the page.

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