1.
Introduction to
Recommendation Systems
Two Decades of Challenge
By Trieu Nguyen - Head of Platform at Blueseed Digital
My personal email: tantrieuf31@gmail.com

2.
Agenda
1. Why do we need recommendation systems ?
2. How can we think with recommendation systems ?
3. How can we implement a recommendation system with
open source technologies ?
a. RFX framework https://github.com/rfxlab
b. Apache Kafka: https://kafka.apache.org
c. Apache Spark: https://spark.apache.org

3.
1 - Why do we need recommendation systems

4.
Recommendation Engine — Examples
● Facebook — “People You May Know”
● Netflix — “Other Movies You May Enjoy”
● LinkedIn — “Jobs You May Be Interested In”
● Amazon — “Customer who bought this item also bought …”
● Google — “Visually Similar Images”
● YouTube — “Recommended Videos”
● Tinder — ”Decide who you want to date”
...

5.
“Recommended for you” by YouTube
User-based Collaboration Filter
Item-based Collaboration Filter

6.
Amazon Recommendation Engine

7.
https://www.computer.org/csdl/mags/ic/2017/03/mic2017030012.html

8.
That my books, mostly from Amazon Recommendations :)

9.
https://www.slideshare.net/xamat/qcon-sf-2013-machine-learning-recommender-systems-netflix-scale
At Netflix

10.
#ChoiceArchitecture is the basic idea behind recommendation

11.
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

12.
The value of product recommendation
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

13.
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:

14.
2 - How can we design recommendation
systems?

15.
Popular techniques to
build recommendation systems
1. Collaborative Filtering
2. Content-Based Filtering
3. Hybrid Recommendation Systems

16.
User-based Collaborative Filtering
● Based on a large amount of information on users’ behaviors, activities or
preferences
● Predicting what users will like based on their similarity to other users.
● A key advantage:
○ accurately recommending complex items such as movies without
requiring an “understanding” of the item itself

17.
User-based Collaborative Filtering

18.
Content Based Filtering
( Item-based collaborative filtering)
Key ideas:
● Based on a description of the item and a profile of the user’s preference.
● Keywords are used to describe the items
● User profile is built to indicate the type of item this user likes.

19.
Content Based Filtering
( Item-based collaborative filtering)

20.
Hybrid Recommendation Systems
These methods can be used to overcome some
of the common problems:
1. cold start (no information on users’ behaviors)
2. sparsity problem (dense matrix)

21.
Hybrid Recommendation Systems

22.
Example of Hybrid Recommendation Systems
User-based Collaboration Filter
Item-based Collaboration Filter

23.
User-based VS Item-based

24.
3 - Building Data Pipeline for
Video Recommendation System

25.
Simple version of Video Data Pipeline
https://github.com/rfxlab/rfx-video-analytics

26.
Simple Recommendation Engine with Apache Spark MLLib

27.
Picture from http://www.rfxlab.com/p/ad.html

28.
https://www.infoq.com/news/2016/09/How-YouTube-Recommendation-Works

29.
The final question:
How computers could know
what we really want ?

30.
Follow this page to get more information
http://BigDataVietnam.org
https://facebook.com/BigDataVN

31.
Ref links about Apache Spark
http://blogs.quovantis.com/recommendation-engine-using-apache-spark/
https://chimpler.wordpress.com/2014/07/22/building-a-food-recommendation
-engine-with-spark-mllib-and-play
http://ampcamp.berkeley.edu/big-data-mini-course/movie-recommendation-wi
th-mllib.html
https://stanford.edu/~rezab/classes/cme323/S16/
https://www.codementor.io/jadianes/building-a-recommender-with-apache-sp
ark-python-example-app-part1-du1083qbw
https://bugra.github.io/work/notes/2014-04-19/alternating-least-squares-meth
od-for-collaborative-filtering/

32.
Ref links
http://dataconomy.com/2015/03/an-introduction-to-recommendation-engines
https://www.tastehit.com/blog/personal-data-in-personalization-and-advertising/
https://medium.com/@humansforai/recommendation-engines-e431b6b6b446
http://infolab.stanford.edu/~ullman/mmds/ch9.pdf
https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-
part-1-55838468f429