3. People don’t know what they want
until you recommend it to them.
“Faster horses” means “Innovation”
4. 3 key ideas
about data
1. Data Product
2. Data Engineering
3. Data Science
1. Recommendation Systems in Practice
2. Types of Recommendation Systems
3. Building Data Pipeline for Video
Recommendation System
3 key ideas
about the slide
16. WHY SHOULD WE USE RECOMMENDATION
ENGINES?
1. Two-thirds of movies watched by Netflix customers are
recommended movies
2. 38% of click-through rates on Google News
are recommended links
3. 35% of sales at Amazon arise from recommended products
Steve Jobs: “A lot of times, people don’t know what
they want until you show it to them.”
17. Beneficial features of the product
recommendation engine to marketers
1. Retain user loyalty
2. Builds the volume of user traffic
3. Delivers more convenient UX to your user
4. Give your business a wider marketplace
18. So what is recommendation
engine ?
In technical terms, a recommendation engine problem is to develop a
mathematical model or objective function which can predict how
much a user will like an item.
If U = {users}, I = {items} then F = Objective function and measures the
usefulness of item I to user U, given by: F: U x I → R
Where R = {recommended items}.
For each user u, we want to choose the item i that maximizes the objective
function:
19. 2 -WHAT ARE THE DIFFERENT TYPES
OF RECOMMENDATION ENGINES?
20. 3 important types of recommender systems
1. Collaborative Filtering
2. Content-Based Filtering
3. Hybrid Recommendation Systems
21. User-based Collaborative
Filtering
Collaborative filtering methods are based on collecting and analyzing a large amount of
information on users’ behaviors, activities or preferences and predicting what users will like
based on their similarity to other users.
A key advantage of the collaborative filtering approach is that it does not rely on machine
analyzable content and therefore it is capable of accurately recommending complex items
such as movies without requiring an “understanding” of the item itself
23. Content Based Filtering
( Item-based collaborative
filtering)
Content-based filtering methods are based on a description of the item and a profile of the
user’s preference. In a content-based recommendation system, keywords are used to
describe the items; beside, a user profile is built to indicate the type of item this user likes.
