Recommender systems are knowledge-based systems which support human decision-making. In an era of overwhelming choice, they help us decide which products, services and information to consume. The focus of attention in recommender systems research and development has been on making recommendations to individual consumers. These places focus on the easier case, but ignore the fact that it is as common, if not more common, for us to consume items in groups such as couples, families and parties of friends. The choice of a date movie, a family holiday destination, or a restaurant for a celebration meal all require the balancing of the preferences of multiple consumers

1. RECOMMENDER
SYSTEMS
Social Network
Based Algorithms
Année Universitaire 2014 - 2015
Realisé par:
ABBAD Zakariae
HIRCHOUA Badr
Master
« Systèmes d’Information, Réseaux et Multimédia»

2. OUTLINE
Algorithms
Conclusion
Introduction
Problematic and challenges
Types of RS in the SN
Our Algorithm
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3. Introduction

4. Introduction
In human history, people have always been trying to
make predictions and forecasts for a range of issues.
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01
02
the election results
Recommanding items to
customers(amazon,
facebook…)

5. Introduction
Recommender systems are widely used in several
different domains for the recommendation of
articles, music, movies, and even people.
Portals such as Amazon and Submarino use
recommender systems to suggest products to their
customers. Meanwhile, social networks such as
LinkedIn and Facebook use them to suggest new
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Applications
2
3
People you may know
Based on mutual friends work and
education information …
Since our formula is automatic, you might
occasionally see people you don’t know or don’t
want to be friends with. To remove them from
view, just click the X next to their names
1
Facebook friend recommendations

7. Introduction
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Social Networks Definition
Social networks are built from a
group of people who share the same
interests, backgrounds, and activities.
They can socially share and upload
files such as images, videos, and
audios to their profiles.
Social networks consist of nodes that
are the actors in the network. These
nodes might be a user, a company…

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Social Networks Definition
There are different properties that social networks provide
two main concepts:
Profiling
where each user has his own profile,
which represents the user’s preferences
and interests
Linking
between users, which make it easier to
analyze relationships among users

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Social Networks Definition
Social graph: the pattern of the social relationships in the social networks

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Social Networks Definition
 Understanding the structure of social networks
will help evaluating the strength , weaknesses,
opportunities and threats associated with them.
 One of the most popular papers is Milgram’s “The
Small-World Problem” where the earliest experiment
about the six degrees of separation was investigated.

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 A lot of types of the recommender systems, have
been studied by many researchers in the past decade.
 However, they ignore the social relationships
among users
 In fact, these relationships can improve the
accuracy of recommendation.
Traditional R.S

13. Problematic
and
challenges

14. Problematic and challenges
Gray
Sheep
Scalability
Early-
Rater
Cold
Start
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 The problem accurs at the start of the system, or
when the system has a new user.
 HowevAutomatically the system does not have any
information of the tastes or preferences of the new
user.
 SO it’s difficult to recommand any item to this new
user.
The cold start problem

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 Wher the user has rare tastes, the recommendation
may not be accurate.
 a user with this profile is not easily related to other
users in the system.
 This situation is making it difficult to recommend
items to this user
The gray sheep problem

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 When a new item emerges, it cannot be
recommended to a user before a person assesses it.
 This issue is clearly identified in collaborative
filtering.
 In content-based filtering, knowing the contents of
an item is enough to enable a recommendation to a
user
The Early rater problem

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 Only items that are similar to those previously
evaluated by the user will be recommended.
 A user whose profile has been defined will always
receive items related to this profile.
 any personal profile modification (outside the
system) will not be reflected on the system.
Super-specialization problem

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 When the quantity of users, items and evaluations is
too large.
 the system that executes real-time calculations of the
relations among users may provide :
The scalability problem

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 How to make good recommendations without violating
privacy concerns
 This is a hot topic that makes the debate in the world
Privacy-preservation

21. Types of RS
in the SN

22. RS as a Research Problem
1) Content-Based Recommendations: It uses information describing the
nature of an item and based on a sample of the users preferences, to
predict which items the user will like.
2) Collaborative Recommendations: It uses a large amount of
information on users behaviors, activities or preferences and predicts
what users will like based on their similarities to other users.
3) Hybrid Recommendations. This is an approach combining collaborative
filtering and content-based filtering.
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24. Types of RS – Content based RS
Content based RS highlights
 Recommend items similar to those users preferred in the past
 User profiling is the key
 Items/content usually denoted by keywords
 Matching “user preferences” with “item characteristics” … works
for textual information
 Vector Space Model widely used
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25. Types of RS – Content based RS
Content based RS – Limitations
 Not all content is well represented by keywords, e.g. images
 Items represented by same set of features are indistinguishable
 Overspecialization: unrated items not shown
 Users with thousands of purchases is a problem
 New user: No history available
 Shouldn’t show items that are too different, or too similar
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26. Types of RS – Collaborative RS
Collaborative RS highlights
 Use other users recommendations (ratings) to
judge item’s utility
 Key is to find users/user groups whose interests
match with the current user
 Vector Space model widely used (directions of
vectors are user specified ratings)
 More users, more ratings: better results
 Can account for items dissimilar to the ones seen
in the past too
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27. Types of RS – Collaborative RS
Collaborative RS - Limitations
 Different users might use different scales. Possible
solution: weighted ratings, i.e. deviations from average
rating
 Finding similar users/user groups isn’t very easy
 New user: No preferences available
 New item: No ratings available
 Demographic filtering is required
 Multi-criteria ratings is required
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Methods for recommendation in SN
1
2
3

29.  Explore the social network for raters
 Aggregate the ratings to compute prediction
 Store the social rating network
 No Learning phase
 Slow in prediction
 Most pioneer works for recommendation in SN are
memory based approaches
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Memory based approaches

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 Learn a model
 Store the model parameters only
 Extra time for learning
 Fast in Prediction
 Most models are based on matrix factorizatio
Model based approaches

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Link Prediction
 Emergence of online social network
The need to get connected to other
people led to link prediction
 Problem Definition
Given a user pair (u,v), estimate the
probability of creation of the link u v
Given a user u, recommend a list of top
users for u to connect to.

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Link prediction vs. Rating
prediction

33. Algorithms

34. Algorithm for each approach
Approaches Heuristic-Based Model-based
Content-based *TF-IDF (information retrieval)
*Clustering
* Bayesian classifiers
*Clustering
*Decision trees
*Artificial neural networks
Collaborative *Nearest neighbor
(cosine, correlation)
*Clustering
*Graph theory
*Bayesian networks
*Clustering
*Artificial neural Networks
*Probablistic models
*Linear regression
Hybrid *Linear combination of
predicted ratings
*Various voting schemes
*Incorporating one
component as a part of the
heuristic
for the other
*Incorporating one
component as a part
of the model for the other
*Building one unifying
model
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35. Our
Algorithm

36. What is Graph ?
5
3
2
4
0
1
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37. Defining Cliques
A Clique in a Graph G is a complete subgraph of
G that is, it is a subset S of the vertices such that
every two vertices in S form an edge in G
Nodes {O | 1 | 4} form a clique
in a social network such a clique is a subset of individuals
who are more closely and intensely tied to one another than
they are to other members of the network
3
2
4
0
1
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38. Defining Maximal Cliques
5
3
2
4
0
1
A Maximal is a clique to which no more vertices
can be added
The pairs {2,3} , {3,4} , {3,5}, {1,2}
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39. Maximum Cliques
 For some problemns, finding subgraphs of a graph
that are complete can be important
 Complete means that for every node in the graph,
it is connected to every other node
Examples :
 Finding sets of people in a social network
that all know each other
 Finding subjects in an infected population
that all have contact with on another
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40. Use of Cliques
 Analyzing communication network
 Designing circuits
 Analyzing gene expression data
 Analyzing social networks
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41.  The basic form of the Bron–Kerbosch algorithm is
recursive backtracking algorithm that searches for all
maximal cliques in a given graph G.
Bron–Kerbosch algorithm
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42. 01
02
03
Without
pivoting
With pivoting
With vertex ordering
Types of Bron–Kerbosch
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43. Bron–Kerbosch algorithm
BK(R, P, X):
if P and X are both empty
report R as a maximal clique
for each vertex v in P:
BK(R ⋃ {v}, P ⋂ N(v), X ⋂ N(v)
P := P {v}
X := X ⋃ {v}
Without pivoting
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44. Bron–Kerbosch algorithm
BronKerbosch2(R,P,X):
if P and X are both empty:
report R as a maximal clique
choose a pivot vertex u in P ⋃ X
for each vertex v in P N(u):
BronKerbosch2(R ⋃ {v}, P ⋂ N(v), X ⋂
N(v))
P := P {v}
X := X ⋃ {v}
With pivoting
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45. Bron–Kerbosch algorithm
BronKerbosch3(G):
P = V(G)
R = X = empty
for each vertex v in a degeneracy
ordering of G:
BronKerbosch2(R ⋃ {v}, P ⋂ N(v), X
⋂ N(v))
P := P {v}
X := X ⋃ {v}
With vertex ordering
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46. Example
BronKerbosch2(Ø, {1,2,3,4,5,6}, Ø)
BronKerbosch2({2}, {1,3,5}, Ø)
BronKerbosch2({2,3}, Ø, Ø): output {2, 3}
BronKerbosch2({2,5}, {1}, Ø)
BronKerbosch2({1,2,5}, Ø, Ø): output {1,2,5}
BronKerbosch2({4}, {3,5,6}, Ø)
BronKerbosch2({3,4}, Ø, Ø): output {3,4}
BronKerbosch2({4,5}, Ø, Ø): output {4,5}
BronKerbosch2({4,6}, Ø, Ø): output {4,6}
BronKerbosch2({6}, Ø, {4}): no output
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47. Example
BronKerbosch3(G)
BronKerbosch2({6}, {4}, Ø)
BronKerbosch2({6,4}, Ø, Ø): output {6,4}
BronKerbosch2({4}, {3,5}, {6})
BronKerbosch2({4,3}, Ø, Ø): output {4,3}
BronKerbosch2({4,5}, Ø, Ø): output {4,5}
BronKerbosch2({3}, {2}, {4})
BronKerbosch2({3,2}, Ø, Ø): output {3,2}
BronKerbosch2({1}, {2,5}, Ø)
BronKerbosch2({1,2}, {5}, Ø)
BronKerbosch2({1,2,5}, Ø, Ø): output {1,2,5}
BronKerbosch2({2}, {5}, {1,3}): no output
BronKerbosch2({5}, Ø, {1,2,4}): no output
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48. Conclusion

49. Social Network Based Recommendation Systems use the
additional information from the social network structures to
improve the performance and accuracy of recommendations.
With the growing number of internet social networks, there
are great potential to utilize this information to help with improving
the recommendation systems. Good recommendation systems can not
only improve the business outcomes but also help with reducing the
information barriers for regular users.
Conclusion
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