Data Science Webinar: Recommender systems used by ecommerce companies

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Copyright © Ivy Professional School - 2009-10 (All Rights Reserved)
Recommender
Systems
in Action
Data Science Series

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Agenda
1. Why are we talking about Data Science?
2. What is Data Science?
3. What do people look for in a Data Scientist?
4. Introduction to Recommendation Systems
5. Approaches using Recommendations Systems
6. Analytics behind Recommendation Systems: Clustering
7. Building a Recommendation System in R
8. QA Session

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Why are talking about Data Science?

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Unprecedented Data Growth

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The Shortage of Data Analysts

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What is Data Science?

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Multi-Level Expertize

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What do people look for in a Data Scientist?

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T-shaped Skill Set

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Recommendation Systems:
A important Data Science Technique

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Recommendations worth a million
What is common in all these brands..?
They provide us with Recommendations…!

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You and I Experience it in the Digital World

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What are Recommendation Systems?
“Recommender systems* are a subclass of information filtering system that seek to predict the
‘rating’ or ‘preference’ that user will give to an item.”
The key aspect of Recommendation Systems is being able to offer accurate
‘product recommendation’ based on customer’s own preferences and viewing history

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Why Recommendation System are used?
-75%rented movies are from recommendation
-38%are more click-through are due to recommendation
-35%are sales from recommendation

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Approaches to Recommendation Systems
*Source: Wikipedia
There are basically two approaches to Recommendation Systems:
- Collaborative Filtering
- Content Filtering

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Collaborative Filtering
Arrives at a recommendation based on a model of prior
user behavior.
The model can be constructed solely from a single user's
behavior or — more effectively — also from the behavior
of other users who have similar traits.
When it takes other users' behavior into account,
collaborative filtering uses group knowledge to form a
recommendation based on like users.

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Collaborative Filtering…Example
Name Men in
Black
Apollo 13 Top Gun Terminator
Amy 5 4 5 4
Bob 3 2 5
Carl 5 4 4
Dan 4 2
User Based Ratings for Movies
Consider suggesting to Carl that he watch “Men in Black”, since Amy rated it highly and Carl
and Amy seem to have similar preferences.
This technique is Collaborative Filtering

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Content Based Filtering
*Source: Wikipedia
Content-based filtering constructs a recommendation on the basis
of a user's historical behavior.

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Content Based Filtering…Example
Historical Behavior of Amy Recommendations for Amy
Liked “Men In Black”
1. It was directed by Barry Sonnenfeld
2. Classified in the genres of action,
adventure, sci-fi and comedy
3. Stars actor Will Smith
What we consider recommending to Amy
1. Barry Sonnenfeld’s movie “Get Shortly”
2. “Jurassic Park”, which is in the genre of
action, adventure and sci-fi
3. Will Smith’s movie ‘Pursuit of Happiness’

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Hybrid Recommendation Systems
For example,
Through Collaborative filtering we determine that
Amy and Carl have similar preferences.
Through content filtering, “Terminator”, which both
Amy and Carl liked, is classified in same genres as
“Starship Troopers”
Recommend “Starship Troopers” to both Amy and
Carl, even though neither of them have seen it before.
Collaborative Filtering Systems
Content Filtering Systems
Hybrid Recommendation Systems

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Analytics in Recommendation Systems

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Movie Lens Item Data
• Movies in the dataset are categorized
as belonging to different genres
• Each movie may belong to many
genres
Can we systematically find
groups of movies with
similar sets of genres?
If yes, how?

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Yes, through a Machine Learning technique
called Clustering
*Source: Wikipedia

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• Clustering is a ‘UnSupervised Method’ of
statistical learning as there is no target
variable to predict
• Here, the objective is to segment the data
into similar groups instead of prediction
• We can cluster data into ‘similar groups’ and
then build a predictive model for each group
Why Clustering…?

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The Methodology of Clustering
*Source: Wikipedia
Two Clusters which is near to each other (based on Euclidean Distance) is clubbed together to from one
cluster

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Displaying the Clustering Process
2 Clusters
Height of vertical lines represents distance between Clusters
5 Clusters

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The Methodology of Clustering
*Source: Wikipedia
Two Clusters which is near to each other (based on Euclidean Distance) is clubbed together to from one
cluster

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Case Study in R
We will use the MovieLens Data to predict
which all movies can be recommended to Amy
based on her historical behavior, where she liked Men in Black

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Question & Answers

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Thank you

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