Explains about the different types of Recommender systems and the advantages and disadvantages of both. There are examples in the presentation which showcase how the recommender systems recommend to the user. Further it explains how they are used by E-commerce platforms to influence user choice.

1. Recommender
Systems
Explained
- Pratik Bhudke

2. Contents:
• Introduction
• What are Recommender Systems ?
• How Recommender Systems work ?
• Approaches used
• Advantages
• Disadvantages
• Ways of recommending
• Evaluation methods of Recommender Systems
• How to make such a system ?
• Recommender System in action.

3. INTRODUCTION

4. In today’s world we are surrounded by choices.
• Which film to watch??
• Which mobile to buy??
• Which book to read??
All these options confuse us…

5. Example:-
Choosing which mobile to buy???
Google PixelSamsungLG Moto G
5” Screen
3 GB RAM
32 GB Internal Memory
16 MP Camera
5” Screen
3 GB RAM
32 GB Internal Memory
16 MP Camera
5” Screen
3 GB RAM
32 GB Internal Memory
16 MP Camera

6. Another example…
Suppose I want to hear a good song. Then how
should I find that song online ??
Also what does “good” mean??
A “good” song can be a romantic song or a
classical song or anything else…

7. Can Google help me ??
Yes, but only if I know the name of song.
But I just want to hear a “good” song…

8. Can Facebook help me ??
Yes, I can see which songs my friends like and
listen to them.
What if I do not like the songs that my friends
like ??

9. Can Experts help me ??
Yes, expert opinions can be very useful.
But their opinions are what they like and not
what I like.

10. So how am I going to find a good song ??
The solution is
Recommender Systems
How ????

11. What are Recommender Systems ???
Recommender systems are a subclass of
“information filtering system” that seek to
predict the “rating” or “preference” that a user
would give to an item.

12. How Recommender Systems work ???
They work in following two ways:
1. Collaborative filtering
2. Content-based filtering

13. Collaborative filtering:
• Builds a model from user’s past behavior
• Analyzes similar decisions made by other
users

14. Collaborative filtering:

15. Example:

16. Disadvantages:
• Cold start problem: Requires past data of
users for recommendation
• So not beneficial for new users.

17. Content-based filtering:
• Uses a series of discrete characteristics of the
item
• Analyzes items having similar characteristics

18. Content-based filtering:

19. Example:

20. Disadvantages:
• Cold start problem: Here the past data of
items are required for recommendation.
• Requires extensive computation for
recommendation
• Example: Colgate may have many different
types of toothbrush as well as toothpaste. So
which one to recommend to whom ???

21. Hybrid approach:
• Combines both: Collaborative and Content-
based
• Uses the advantages of both methods.

22. Hybrid approach:

23. Advantages:
• Doesn’t depend upon past user data. (Because
it can analyze data of similar users to
recommend)
• Since we have analyzed data, extensive
computing is not required.

24. Example:

25. Ways of recommending:
• Promotion of certain items
• Interactivity on your platform
• Customer feedbacks

27. Evaluation methods of Recommender systems:
• Test with real users
• A/B tests
• Does sale increase or decrease after
change ?

28. • Lab studies
• Perform controlled experiments
• User satisfaction with system
(questionnaires)
Evaluation methods of Recommender systems:

29. • Offline experiments
• Based on historical data
• Prediction accuracy of system
Evaluation methods of Recommender systems:

30. Effects of Recommender system:
• Increases sales of particular item
• Increases sales of other related items

31. How to make a Recommender system ???
• Open Source Tools:
• Apache Mahout : Java
• Weka : Java
• Shogun : Java as well as C#

32. So lets see a
Recommender System
in action

33. Conclusion:
• Recommender systems are very useful for
users in decision making.
• But we should be careful that these systems
can also be configured to influence bad
recommendations.