Product Recommendations Engine
A recommendation engine involves connecting users to products/ items.
Let’s 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).
| P1 | P2 | P3 | P4 | |
| C1 | 4 | 1 | 5 | |
| C2 | 5 | 3 | ||
| C3 | 3 | ? | ||
| C4 | 2 | 3 |
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- 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 to what recommender systems are all about, I will discuss in detail more of these methods in my future posts.
