1. RECOMMENDER SYSTEMS AND
SEARCH ENGINES – TWO SIDES OF
THE SAME COIN!?
Bracha Shapira
Lior Rokach
Department of Information Systems Engineering
Ben-Gurion University

2. CONTENT
 Introduction
 Applications
 Methods
 Recommender Systems vs. search engines

3. 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

4. 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

5. EXAMPLE APPLICATIONS

6. WHAT BOOK SHOULD I BUY?

7. 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.

8. 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.
13.10.2012

9. WHAT NEWS SHOULD I READ?

10. 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.

11. Usage in the market/products Recommendation
State-of-the-art solutions
Examined Solutions
Method Commonness
Jinni Taste Kid Nanocrowd Clerkdogs Criticker IMDb Flixster Movielens Netflix Shazam Pandora LastFM YooChoose Think Analytics Itunes Amazon
Collaborative Filtering v v v v v v v v v v v v
Content-Based Techniques v v v v v v v v v v v
Knowledge-Based Techniques v v v v v v v
Stereotype-Based Recommender Systems v v v v v v v
Ontologies and Semantic Web Technologies for
v v v
Recommender Systems
Hybrid Techniques v v v v v v v
Ensemble Techniques for Improving
v future
Recommendation
Context Dependent Recommender Systems v v v v v v
Conversational/Critiquing Recommender
v v
Systems
Community Based Recommender Systems and
v v v v v
Recommender Systems 2.0
13.10.2012

12. COLLABORATIVE FILTERING

13. Collaborative Filtering
Description
 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 1 Collaborative Filtering
those who agreed in the past tend to agree again in
the future.
Selected Techniques
kNN - Nearest Neighbor 
SVD – Matrix Factorization 
Similarity Weights Optimization (SWO) 
13.10.2012

14. 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
13.10.2012

15. How collaborative filtering works?
“People who liked this also liked…”
abcd abcd
How it works 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
Item users.
to
Item 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
User to liked Item 4 will also like item 1, so Item 1 will be
recommended to Tim. This approach is scalable to
User millions of users and millions of items.
13.10.2012

16. KNN - NEAREST NEIGHBOR
Current User Users
1 1st item rate
0 Dislike
?
1
0
1 Like
abcd Prediction
abcd
Unknown Rating
abcd
Other Users
1
This user did not   There are other
Items
? 1  The prediction
Unknown rate the item. We
was made based users who rated
0 will try to predict a
on the nearest the same item.
rating according We are interested
1 neighbor.
to his neighbors. in the Nearest
abcdHamming Distance
1  The Hamming distance Neighbors.
is named
after Richard Hamming.
0
User Model = 1
 In information theory, the Hamming
distance between two strings of
interaction
abcd
Nearest Neighbors equal length is the number of
 We are looking
1 positions at which the corresponding abcd
history the Nearest 1
symbols are different.
for
Neighbor. The Nearest 
one with the 1 Neighbor
lowest Hamming 0 14th item rate
distance.
Hamming 5 6 6 5 4 8
distance
13.10.2012

17. IMPORTANT ISSUES
 Cold Start
 Implicit/Explicit Rating
 Sparsity
 Long Tail problem - many items in the Long Tail have only
few ratings
 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
 Gray sheep problem
 Iformation Security
 Misuse
 Privacy

18. CONTENT-BASED RECOMMENDER
SYSTEM

19. 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.

20. SIMPLE EXAMPLE
Read update
User Profile
New books Match User Profile
Recommender
Systems recommendation

21. CONTEXT-BASED RECOMMENDER
SYSTEMS

22. 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
13.10.2012

23. 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.
13.10.2012

24. 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.
13.10.2012

25. Information Discovery: Example
“Tell me the music that I want to listen NOW"
abcd abcd
Musicovery.com 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.
13.10.2012

26. 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 ?
13.10.2012

27. 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.
13.10.2012

28. 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?
13.10.2012

29. 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
13.10.2012

30. SOCIAL (TRUST) BASED
RECOMMENDER SYSTEMS

31. 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.
.
13.10.2012

32. TRUST- BASED COLLABORATIVE FILTERING
Active users’ trusted
friends
Active user
3
?
Rating
prediction

33. TRUST METRICS
 Global metrics: computes a single global trust value for
every single user (reputation on the network)
b
 Pros: d
a
 Based on the whole community opinion
c
 Cons:
 Trust is subjective (controversial users)

34. 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:
 Ignoring the “wisdom of the crowd” b
a d
c

35. SEARCH ENGINES AND
RECOMMENDER SYSTEMS

36. SEARCH ENGINES VS. RECOMMENDER SYSTEMS –
Search Engines Recommender Systems
 Goal – answer users ad  Goal – recommend services
hoc queries or items to user
 Input – user ad-hoc need  Input - user preferences
defined as a query defined as a profile
 Output- ranked items
relevant to user need  Output - ranked items based
(based on her on her preferences
preferences???)
 Methods - Mainly IR
 Methods – variety of
based methods methods, IR, ML, UM

37. 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

38. SEARCH ENGINES PERSONALIZATION METHODS
ADOPTED FROM RECOMMENDER SYSTEMS
 Collaborative filtering
 User-based - Cross domain collaborative filtering is
required???
 Content-based
 Search history
 Collaborative content-based
 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