This document discusses the use of data science in recommendation systems. It defines recommendation systems as systems that predict a user's preferences for items and recommend top items. It also defines data science as using scientific methods to extract knowledge from structured and unstructured data. The document then describes different types of recommendation systems, including collaborative filtering, content-based filtering, and hybrid systems. It provides examples of how Netflix, Amazon, LinkedIn, and Pandora use recommendation systems.

1. Use Of Data Science in
Recommendation System

2. What is Data Science
• Inter-disciplinary field that uses scientific
methods, processes, algorithms and
systems to extract knowledge and insights
from structured and unstructured data.
• It uses techniques and theories drawn from
many fields within the context of
mathematics, statistics, computer science,
information science, and domain
knowledge.

3. What is a Recommendation
system?
• System that predicts the future
preference of a set of items for a
user and recommend the top
items.
• People have too much options to
use from due to the prevalence of
Internet
• General areas of application.

4. Types of Recommendation
Systems
• Collaborate Filtering
• Content Based Filtering
• Hybrid Recommendation System

5. Collaborative Filtering
Collaborative filtering methods finds a subset of users
who have similar tastes and preferences to target user
and use this subset for offering recommendations
Basic Assumptions:
• Users with similar interests have common
preferences
• Sufficiently large number of user preferences are
available
Main Approaches:
• User Based
• Item Based

6. User-User
Collaborative Filtering
• Here, we try to search for lookalike
customers and offer products based
on what his/her lookalike has
chosen.
• Lot of time and resources required.
• This type of filtering requires
computing every customer pair
information which takes time. So, for
big base platforms, this algorithm is
hard to put in place.

7. Item-Item
Collaborative Filtering
• Here, we try finding item lookalike. Once
we have item lookalike matrix, we can
easily recommend alike items to a
customer who has purchased any item
from the store.
• Requires fewer resources than user-user
collaborative filtering.
• Amazon uses this approach in its
recommendation engine to show related
products which boost sales.

8. Collaborative-Based
Filtering
Advantages Disadvantages
No domain
knowledge
necessary
Cannot handle fresh items
Serendipity Hard to include side
features for query/item
Great starting
point

9. Content-based filtering
• Based on the description of an item and a
profile of the user’s preferred choices.
• Keywords are used to describe the items
• The algorithms tries to recommend products
which are like the ones that a user has liked in
the past.
• This approach has its roots in information
retrieval and information filtering research.

10. Content-Based
Filtering
Advantages Disadvantages
No data about the user
required
This technique requires a lot of
domain knowledge. Therefore,
the model can only be as good
as the hand-engineered
features.
The model can capture the
specific interests of a user,
and can recommend niche
items that very few other
users are interested in.
The model can only make
recommendations based on
existing interests of the user. In
other words, the model has
limited ability to expand on the
users' existing interests.

11. Hybrid Recommendation
systems
• Combination of collaborative and content-based
recommendation.
• Hybrid approaches can be implemented by making
both the predictions separately and then combining
them.
• Netflix is a good example of the use of hybrid
recommender systems.
• The website makes recommendations by comparing
the watching and searching habits of similar users as
well as by offering movies that share characteristics
with films that a user has rated highly.

12. What is Cold Start in
Recommendation System?
• Cold start is a potential problem in
computer-based information
systems which involves a degree of
automated data modelling.
• There are three cases of cold start
1. New community
2. New user
3. New item

13. How can Cold Start
be Neglected?

14. Why are recommender
systems important?
• They predict whether a particular user would
prefer an item or not based on the user’s profile.
• They reduce transaction costs of finding and
selecting items in an online shopping
environment .
• In an e-commerce setting, recommender systems
enhance revenues, for the fact that they are
effective means of selling more products .
• In scientific libraries, recommender systems
support users by allowing them to move beyond
catalogue searches.

15. Recommendation
System Example
Facebook : People You May Know
YouTube : Recommended Videos
Netflix : Movies you may enjoy
Google : Search result adjusted
LinkedIn : Jobs you may be interested in
Pinterest : Recommended Images
Amazon : Product Marketting

16. Netflix Using
Recommender System
• What is Netflix?
• Machine Learning in Netflix.
• Netflix’s Key Features.
• How Netflix’s Recommendation
System works?

17. Amazon Using
Recommender System
• What is Amazon?
• Machine Learning in Amazon.
• Amazon’s Key Features.
• How Amazon’s Recommendation
System works?

18. LinkedIn Using
Recommender System
• What is LinkedIn?
• Reverse Engineering of LinkedIn’s
recommendation system.
• The Data Science Team at
LinkedIn.

19. Pandora and Music
Genome Project
• What is Pandora?
• Music Genome Project – 450
attributes
• History of Music Genome Project
• 5 sub - Genomes – Pop/Rock, Hip-
Hop/Electronica, Jazz, World
Music and Classical

20. Pandora Leverage Using
Recommender System
• How does Pandora’s Music
Recommendation work?
• Thumbs Up and Thumbs Down to
a song
• How is Pandora different from
other similar music streaming
services?

21. References
• Introduction to recommender systems, an
overview of some major recommendation
algorithms. – By Baptiste Rocca and Joseph Rocca
• Francesco Ricci and Lior Rokach and Bracha
Shapira, Introduction to Recommender Systems
Handbook, Recommender Systems Handbook,
Springer, 2011, pp. 1-35
• Prem Melville and Vikas Sindhwani,
Recommender Systems, Encyclopedia of Machine
Learning, 2010.

22. Thank You