RECOMMENDER SYSTEMS AND
SEARCH ENGINES – TWO SIDES
OF THE SAME COIN!?
Bracha Shapira
Lior Rokach
Department of Information Systems Engineering
Ben-Gurion University
CONTENT
Introduction
 Applications
 Methods
 Recommender Systems vs. search engines

ARE YOU BEING SERVED?



What are you looking for?
Demographic – Age, Gender, etc.
Context



Casual/Event
Season
Gift
Purchase History


Loyal Customer
What is the customer currently wearing?



Style
Color
Social


Friends and Family
Companion
RECOMMENDER SYSTEMS

A recommender system (RS) helps people
that have not sufficient personal experience or
competence to evaluate the, potentially
overwhelming, number of alternatives offered
by a Web site.
In their simplest form RSs recommend to their
users personalized and ranked lists of items
 Provide consumers with information to help
them decide which items to purchase

4
EXAMPLE APPLICATIONS
WHAT BOOK SHOULD I BUY?
6
WHAT MOVIE SHOULD I WATCH?
• The Internet Movie Database (IMDb)
provides information about actors,
films, television shows, television
stars, video games and production
crew personnel.
• Owned by Amazon.com since 1998
• 796,328 titles and 2,127,371 people
• More than 50M users per month.
7
abcd
The Nextflix prize story
 In October 2006, Netflix announced it would give a $1 million to whoever
created a movie-recommending algorithm 10% better than its own.
 Within two weeks, the DVD rental company had received 169 submissions,
including three that were slightly superior to Cinematch, Netflix's
recommendation software
 After a month, more than a thousand programs had been entered, and the top
scorers were almost halfway to the goal
 But what started out looking simple suddenly got hard. The rate of
improvement began to slow. The same three or four teams clogged the top of the
leader-board.
 Progress was almost imperceptible, and people began to say a 10 percent
improvement might not be possible.
 Three years later, on 21st of September 2009, Netflix announced the winner.
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8
WHAT NEWS SHOULD I READ?
9
WHERE SHOULD I SPEND MY VACATION?
Tripadvisor.com
I would like to escape from this ugly an tedious work life and
relax for two weeks in a sunny place. I am fed up with
these crowded and noisy places … just the sand and the
sea … and some “adventure”.
I would like to bring my wife and my children on a
holiday … it should not be to expensive. I prefer
mountainous places… not too far from home.
Children parks, easy paths and good cuisine are a
must.
I want to experience the contact with a completely different
culture. I would like to be fascinated by the people and
learn to look at my life in a totally different way.
10
Usage in the market/products Recommendation
State-of-the-art solutions
Method
Commonness
Examined Solutions
Jinni Taste Kid Nanocrowd
Clerkdogs Criticker IMDb Flixster Movielens
Collaborative Filtering
v
Content-Based Techniques
v
v
v
v
v
Knowledge-Based Techniques
v
v
v
v
v
Stereotype-Based Recommender Systems
v
v
v
v
v
Ontologies and Semantic Web
Technologies for Recommender Systems
v
v
Hybrid Techniques
v
v
Ensemble Techniques for Improving
Recommendation
Context Dependent Recommender
Systems
Conversational/Critiquing
Recommender Systems
v
Community Based Recommender
Systems and Recommender Systems 2.0
v
v
v
v
v
Netflix
v
Shazam Pandora LastFM YooChoose Think Analytics Itunes Amazon
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
v
future
v
v
v
v
v
v
v
v
v
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COLLABORATIVE FILTERING
 The method of making automatic predictions
(filtering) about the interests of a user by
collecting taste information from many users
(collaborating). The underlying assumption of
CF approach is that those who agreed in the
past tend to agree again in the future.
1
Selected Techniques
Description
Collaborative Filtering
Collaborative Filtering
kNN - Nearest Neighbor 
SVD – Matrix Factorization 
Similarity Weights Optimization 
(SWO)
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COLLABORATIVE FILTERING
abcd
The Idea
 Trying to predict the opinion the user will have on the different items
and be able to recommend the “best” items to each user based on: the
user’s previous likings and the opinions of other like minded
users
Negative
Rating
Positive
Rating
?
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How collaborative filtering works?
“People who liked this also liked…”
abcd
How it works
Item
to
Item
User to
User
abcd
User-to-User
 Recommendations are made by finding users
with similar tastes. Jane and Tim both liked
Item 2 and disliked Item 3; it seems they might
have similar taste, which suggests that in
general Jane agrees with Tim. This makes
Item 1 a good recommendation for Tim.
This approach does not scale well for millions
of users.
Item-to-Item
 Recommendations are made by finding items
that have similar appeal to many users.
Tom and Sandra are two users who liked both
Item 1 and Item 4. That suggests that, in
general, people who liked Item 4 will also like
item 1, so Item 1 will be recommended to Tim.
This approach is scalable to millions of users
and millions of items.
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KNN
- NEAREST NEIGHBOR
Current User
distance.
1st item rate
abcd
abcd
Unknown
Prediction
Rating
abcd
Other Users
This user did not 
 There are other
 The prediction
rate the item. We
users who rated
was made based
will try to predict a
the same item.
on the nearest
rating according
We are interested
neighbor.
to his neighbors.
abcd
in the Nearest
Hamming
Distance
 The Hamming distance Neighbors.
is named
after Richard Hamming.
 In information theory, the Hamming
distance between two strings of
equal length is the number of
positions at which the corresponding
symbols are different.
Items
1
0
Dislike
?
1
0
1
Like
1
1
?
Unknown
0
1
1
0
User Model
= 1
abcd
Nearest Neighbors
1
interaction
 We are looking
the Nearest 1
historyfor
Neighbor. The
1
one with the
lowest Hamming 0
Users
abcd
Nearest 
Neighbor
14th item rate
Hamming
distance
5
6
6
5
4
8
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IMPORTANT ISSUES
Cold Start
 Implicit/Explicit Rating
 Sparsity



Portfolio Effect: Non Diversity Problem


It is not useful to recommend all movies by Antonio
Banderas to a user who liked one of them in the past
Beyond Popularity


Long Tail problem - many items in the Long Tail
have only few ratings
Gray sheep problem
Iformation Security
Misuse
 Privacy

CONTENT-BASED
RECOMMENDER SYSTEM
CONTENT-BASED RECOMMENDATION
In content-based recommendations the system tries to
recommend items that matches the User Profile.
 The Profile is based on items user has liked in the past
or explicit interests that he defines.
 A content-based recommender system matches the
profile of the item to the user profile to decide on its
relevancy to the user.

19
SIMPLE EXAMPLE
update
Read
New books
Match
Recommender
Systems
20
User Profile
recommendation
User Profile
CONTEXT-BASED RECOMMENDER
SYSTEMS
Context-Based Recommender Systems
abcd
Overview
 The recommender system uses additional data about the context of an
item consumption.
 For example, in the case of a restaurant the time or the location may
be used to improve the recommendation compared to what could be
performed without this additional source of information.
 A restaurant recommendation for a Saturday evening when you go
with your spouse should be different than a restaurant
recommendation on a workday afternoon when you go with co-workers
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Context-Based Recommender Systems
Motivating Examples
 Recommend a vacation
 Winter vs. summer
 Recommend a purchase (e-retailer)
 Gift vs. for yourself
 Recommend a movie
 To a student who wants to watch it on Saturday
night with his girlfriend in a movie theater.
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Context-Based Recommender Systems
Motivating Examples
 Recommend music
 The music that we like to hear is greatly affected by a context, such
that can be thought of a mixture of our feelings (mood) and the
situation or location (the theme) we associate it with.
 Listen to Bruce Springteen "Born in USA" while driving along the 101.
 Listening to Mozart's Magic Flute while walking in Salzburg.
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Information Discovery: Example
“Tell me the music that I want to listen NOW"
abcd
Musicovery.com
abcd
Details
 An Interactive personalized
WebRadio
 A mood matrix propose a
relationship between music
and mood.
 Ethnographic studies have
shown that people choose
music peaces according to
their mood or mood change
expectation.
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Context-Based Recommender Systems
What simple recommendation techniques ignore?
 What is the user
when asking for a recommendation?
 Where (and when) the user is
?
 What does the user
(e.g., improve his knowledge or really buy
a product)?
 Is the user
or with other
?
 Are there
products to choose or only
?
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Context-Based Recommender Systems
What simple recommendation techniques ignore?
 What is the user
when asking for a recommendation?
 Where (and when) the user is
?
 What does the user
(e.g., improve his knowledge or really buy
a product)?
 Is the user
or with other
?
 Are there
products to choose or only
?
Plain recommendation technologies forget to take
into account the user context.
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Context-Based Recommender Systems
abcd
Major obstacle for contextual computing
 Obtain sufficient and reliable data describing the user context
 Selecting the right information, i.e., relevant in a particular
personalization task
 Understand the impact of contextual dimensions on the
personalization process
 Computational model the contextual dimension in a more classical
recommendation technology
 For instance: how to extend Collaborative Filtering to include
contextual dimensions?
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Context-Based Recommender Systems
abcd
Item Split - Intuition and Approach
 Each item in the data base (
) is a candidate for splitting
 Context defines (
) all possible splits of an item ratings vector
 We test all the possible splits – we do not have many contextual
features
 We choose one split (using a single contextual feature) that maximizes
an impurity measure and whose impurity is higher than a threshold
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SOCIAL (TRUST) BASED
RECOMMENDER SYSTEMS
Social Based (Trust based) Recommender Systems
abcd
Overview
 Intuition – Users tend to receive advice from people they trust, i.e.,
from their friends.
 Trusted friends can be defined explicitly by the users or inferred from
social networks they are registered to.
.
31
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TRUST- BASED COLLABORATIVE FILTERING
Active users’ trusted
friends
Active user
3
?
Rating
prediction
TRUST METRICS

Global metrics: computes a single global trust
value for every single user (reputation on the
network)
b
1

Pros:


Based on the whole community opinion
Cons:

Trust is subjective (controversial users)
3
3
a
2
d
c
3
TRUST METRICS (CONT.)
Local metrics: predicts (different) trust scores
that are personalized from the point of view of
every single user
 Pros:




More accurate
Attack resistance
Cons:

b
1
Ignoring the “wisdom of the crowd”
5
?
a
2
d
c
3
SEARCH ENGINES AND
RECOMMENDER SYSTEMS
SEARCH ENGINES VS. RECOMMENDER SYSTEMS –
Search Engines
 Goal – answer users
ad hoc queries
 Input – user ad-hoc
need defined as a
query
 Output- ranked items
relevant to user need
(based on her
preferences???)
 Methods - Mainly IR
based methods
Recommender Systems
 Goal – recommend
services or items to user
 Input - user preferences
defined as a profile


Output - ranked items
based on her preferences
Methods – variety of
methods, IR, ML, UM
NEW TRENDS …
“Understand” the user actual needs from her context
 Personalize results according to the user preferences


Search engines may use some recommender
systems methods to achieve these goals
SEARCH ENGINES PERSONALIZATION METHODS
ADOPTED FROM RECOMMENDER SYSTEMS

Collaborative filtering


Content-based


Search history
Collaborative content-based


User-based - Cross domain collaborative filtering is
required???
Collaborate on similar queries
Context-based
Little research – difficult to evaluate
 Locality, language, calendar


Social-based
Friends I trust relating to the query domain
 Notion of trust, expertise
