The document provides an overview of recommendation systems, detailing their function as information filtering systems that predict user preferences for various items. It discusses the history, benefits, types, and challenges of these systems, emphasizing their role in increasing user engagement, sales, and operational efficiency in e-commerce. Key methods such as collaborative and content-based filtering are explained, alongside notable examples like Amazon and Netflix.

1. PERSONALIZATION & RECOMMENDATION DEMYSTIFIED
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MAXIME LEMAITRE – 03/07/14

2. Agenda
• Introduction
• Brief History
• Paradigms
• An example
• This is not ended
Recommender/recommendation
systems/engines are a subclass
of information filtering system
that seek to predict the rating or
preference that user would give
to an item

3. Recommendations are everywhere
Movies, Social, Books, Music, News …

4. Recommendations are everywhere
Commons requirements, many usages
An online music service with
20 millions of songs …
10 millions of users …
How to
recommend –
pertinent- music
to each user ?

5. Drive Traffic
A recommendation engine can bring traffic to your site.
(with personalized email messages and targeted blasts)
Deliver Relevant Content
By analyzing the customer’s current site usage and his
previous browsing history, a recommendation engine can
deliver relevant product recommendations as he shops. The
data is collected in real-time so the software can react as his
shopping habits change.
Engage Shoppers
Shoppers become more engaged in the site when
personalized product recommendations are made. They are
able to delve more deeply into the product line without
having to perform search after search.
Convert Shoppers to Customers
Converting shoppers into customers takes a special touch.
Personalized interactions from a recommendation
engine show your customer that he is valued as an
individual. In turn, this engenders his loyalty.
Reduce Workload and Overhead
Using an engine automates creation of a personal shopping
experience, easing the workload of your IT staff and your
budget.
5
Recommendation System Benefits (TL;DR)
Increase Order Value / Number of Items per Order
Average order values typically go up when
a recommendation engine in uses to display personalized
options. Advanced metrics and reporting can definitively
show the effectiveness of a campaign.
When the customer is shown options that meet his interest,
he is more likely to add items to his purchase.
Control Merchandising and Inventory Rules
A recommendation engine can add your own marketing and
inventory control directives to the customer’s profile to
feature products that are promotionally prices, on clearance
or overstocked. It gives you’re the flexibility to control what
items are highlighted by the recommendation system.
Provide Reports
Providing reports is an integral part of a personalization
system. Giving the client accurate and up to the minute
reporting allows him to make solid decisions about his site
and the direction of a campaign.
Offer Advice and Direction
An experienced provider can offer advice on how to use the
data collected and reported to the client. Acting as a partner
and a consultant, the provider should have the know-how to
help guide the ecommerce site to a prosperous future.

6. 6
A brief History
Recommenders are older than you might think
1999-2000
• The introduction and vast
success of the Amazon
recommendation engine in the
early 2000s led to wide
acceptance of the technology
as a way of increasing sales
Late 1970s
• Recommendation systems have their
roots in Usenet, a worldwide
distributed discussion system
originating at Duke University
2006
•Netflix Prize Boosted
researches in this
area
Early 2000s
• In addition to
Amazon, many
companies make
recommendations a
core value add of
their services
Late 2000s
• Big Data. How to build
large scale & real-time
recommendation
engines ?

7. The Netflix Prize
http://www.netflixprize.com/
“a $1 million prize for improving Netflix recommendations by 10%”
• Netflix is an online DVD-rental service
• Recommendation algorithm is the core of their business.
– Their whole business model is around cross selling products (movies) to consumers
– The better it works, the more money they stand to make.
• Netflix's own algorithm is called Cinematch
• About the Data : 100,480,507 ratings that 480,189 users gave to 17,770 movies
• Won in 2009, but was a fantastic booster for this area
Recommender system is an active research area in the data mining and machine
learning areas. Some conferences such as RecSys, SIGIR, KDD have it as a topic…

8. “The Web, they say, is leaving the era of search and entering one of discovery. What's
the difference? Search is what you do when you're looking for something. Discovery is
when something wonderful that you didn't know existed, or didn't know how to ask
for, finds you.”, Fortune Magazine
8
Recommendation != Search Engine
Recommendation Engine
Predict how much a user will like an
item that is unknown for him/her
based on context, preferences,
friends, similarity, location, …
DISCOVER
Search Engine
Index and retrieve by criteria similar
documents based exclusively on
content
FIND
( But search is starting to take user into account … )

9. Recommendations are
just ranked list for a user
9
Recommendation as a dedicated function
Item A
Item A
Item A
Item A
Item A
Items
Item X
Item Y
Item Z
Recommendation
Engine
Item A
Item A
Item A
Item A
Item A
Users
User A
Most of recommender
systems are capable of
accurately recommending
complex items without
requiring an "understanding"
of the item itself

10. • Collaborative filtering
filtering methods 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
• Content-based filtering
filtering methods based on a description of the item and a profile of the
user’s preference. Keywords/Meta are used to describe the items; beside, a
user profile is built to indicate the type of item this user likes
• Hybrid Recommender Systems
Mix collaborative filtering and content-based filtering in several ways ; it
could be more effective in some cases
10
Paradigms

11. • The most prominent approach to generate recommendations
– used by large, commercial e‐commerce sites
– well‐understood, various algorithms and variations exist
– applicable in many domains (book, movies, DVDs, ..)
• Approach
– use the "wisdom of the crowd" to recommend items
• Basic assumption and idea
– Users give ratings to catalog items (implicitly or explicitly)
– Customers who had similar tastes in the past, will have similar tastes in the fu
ture
11
Paradigms – Collaborative Filtering
The most prominent approach to generate recommendations

12. Paradigms – Collaborative Filtering
Plethora of different techniques proposed in the last years
• Memory‐based approaches
– the rating matrix is directly used to find neighbors / make predictions
– does not scale for most real‐world scenarios
– large e‐commerce sites have tens of millions of customers and millions of ite
ms
Ex : kNN, Slope One …
• Model‐based approaches
– based on an offline pre‐processing or "model‐learning" phase
– at run‐time, only the learned model is used to make predictions
– models are updated / re‐trained periodically
– large variety of techniques used
– model‐building and updating can be computationally expensive
Ex : Matrix Factorization (SVD), clustering models, Bayesian networks,
probabilistic Latent Semantic Analysis , … 12

13. 13
Neighborhood-based Collaborative Filtering

14. 14
User-based Collaborative Filtering (1/6)

15. 15
User-based Collaborative Filtering (2/6)
Dimensions
Vectors

16. 16
User-based Collaborative Filtering (3/6)

17. 17
User-based Collaborative Filtering (4/6)

18. 18
User-based Collaborative Filtering (5/6)

19. 19
User-based Collaborative filtering (6/6)
Items Bought
By User1

20. • Sparse data
Most users do not rate implicitly/explicitly most items. Less data means
recommendations may be irrelevant.
• Scalability
CF algorithms computation time grows with the number of items and users.
Big data processing requires dedicated infrastructures & components
(Hadoop, MapReduce, HDInsight, Cloud, …)
• Cold Start
Require a large amount of existing data on a user in order to make accurate
recommendations. New users/items to information to leverage.
– New user : never gave feedbacks
– New item : never rated
20
Collaborative filtering
Challenges and issues

21. • Evaluating Recommender Systems
– Is a RS efficient with respect to a specific criteria like accuracy, user
satisfaction, response time, serendipity, online conversion, …
– Do customers like/buy recommended items?
– Do customers buy items they otherwise would have not?
– Are they satisfied with a recommendation after purchase?
21
The is not the end
Let data speak for itself
Netflix’s
workflow

22. 22
Make sure it is needed
ACM Conference, Barcelona, 2010

23. Questions

24. References
• http://en.wikipedia.org/wiki/Recommender_system
• http://en.wikipedia.org/wiki/Collaborative_filtering
• http://en.wikipedia.org/wiki/Slope_One
• http://www.slideshare.net/ErnestoMislej/recommender-systems-asai-2011
• http://www.slideshare.net/torrens/top-10-lessons-learned-developing-deploying-and-operating-realworld-
recommender-systems-5351028
• http://www.recsyswiki.com/wiki/Main_Page
• http://www.slideshare.net/WorapotJakkhupan/basic-of-recommender-system
• http://pkghosh.wordpress.com/2010/10/19/recommendation-engine-powered-by-hadoop-part-1/
• http://web4.cs.ucl.ac.uk/staff/jun.wang/blog/topics/research-resources/collaborative-filtering/
• http://techblog.netflix.com/2012/06/netflix-recommendations-beyond-5-stars.html
• http://www.hindawi.com/journals/aai/2009/421425/
• http://www.certona.com/recommendation-software/benefit-of-recommendation-engines
• http://www.recommenderbook.net/teaching-material
• http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
• http://www.slideshare.net/kerveros99/essir-2013-recsysfinal-25957057
• https://github.com/neo4j-contrib/graphgist/wiki

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