Recommendation System

implement a highly effective cross and up-selling marketing strategy

by Mohamed Jendoubi, founder @ uluumy

The article sums-up the section Recommend from our course Become a Citizen Data Scientist
In this article, we will cover the basics concepts about recommendation system.

Definition

“Recommendation systems are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item.”. Source: Wikipedia

Two primary methodologies

   •   Collaborative Filtering: the item recommended to the user is based on the past purchase and preference of similar users
   •   Content-based filtering: Based on the attributes of items purchased by the user, suggest items with similar properties.
   •   Best examples: Amazon, Netflix

Example

Let’s look at how these two methods work using this very simple example of movies rating.
Here is the example: we have a list of 6 movies (items) and 7 users. Each has rated the movies that she watched (from 1 to 5 stars).
Daniel has not seen the movie "The Notebook"
We want to decide if we will recommend this movie to him or not based on a prediction of his rating for the movie.
So let’s start with a collaborating filtering approach.

Collaborative Filtering Approach

Daniel has not seen the movie "The Notebook"
1. We select the subgroup of users who watched the same movies as Daniel and also who watched "The Notebook".
2. Among this group, we select the users who are "similar" to Daniel in term of rating (for example using KNN algorithm).
3. We compute the average rating that Daniel's "neighbors" gave to "The Notebook".
4. It gives as the predicted rating of Daniel for the movie "The Notebook"
5. We repeat the steps 1 to for 4 for all movies that Daniel haven't seen
6. We recommend Daniel the best predicted rated movies.

Content-based Filtering Approach

We want to predict the Daniel rating for the movie "The Notebook"
Use the similarity between items (in our example movies), and not users, to make predictions
1. We select the movies that are similar to "The Notebook". Based on the genre we can divide movies into to groups "Action" (Skyfall, Star War, X-Men) and "Romance" ("P. S I Love You", "Titanic", "The Notebook")
2. Daniel have rated "P. S I Love You" and "Titanic" which are similar to "The Notebook". Based on his rating of these two movies, we give a predicted rating of "The Notebook"
3. We repeat the steps 1 and 2 for all movies that Daniel haven't seen
4. We recommend Daniel the best predicted rated movies.


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Photos Credits:
London Bike Show 2013 by Jon Arm CC BY

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