This document describes a framework for a semantic social network-based expert recommender system. The framework constructs expert profiles using text and semantic enrichment, builds a semantic social network to detect expert communities, and provides recommendations by matching a user's information needs to relevant expert communities. A case study applying the framework to 315 computer science academics achieved accurate expert recommendations and paper assignments. The framework demonstrates how semantic social networks and community detection can improve recommendation accuracy over traditional collaborative filtering.

1. A SEMANTIC SOCIAL
NETWORK-BASED EXPERT
RECOMMENDER SYSTEM
Elnaz Davoodi, Keivan Kianmehr, Mohsen
Afsharchi
Negotiated Studies
Presentation on

2. BACKGROUND INFORMATION
 Publisher : Springer
 Journal : Applied Intelligence -The International Journal of
Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies
 Date : October 2012
 Keywords : Semantic information extraction ,
Social
network analysis , Expert recommender system ,
Knowledge management

3. OUTLINE
 Abstract
 Introduction to Recommendation
System
 The proposed framework
 A case study
 Performance analysis
 Conclusions

4. ABSTRACT
 Presents a framework to build a hybrid expert
recommendation system that integrates the
characteristics of content-based recommendation
algorithms into a social network-based
collaborative filtering system
 Aims to improve the accuracy of recommendation
prediction by considering the social aspect of
experts’ preferences.

5. INTRODUCTION TO RECOMMENDATION
SYSTEMS
 First used by internet retailers to recommend products to
customers
 In general ,recommendation systems provide personalized
recommendations of items to users based on their previous
behavior, item descriptions, and user preferences
Recommendation Approaches
1 )Content based -provide recommendations by comparing
attributes of an item
2) Collaborative filtering - recommendations by looking
for users who share the same patterns (like-minded users)
with the active user
3) Hybrid methods -combining the content-based and
collaborative filtering recommendation system

6. IDENTIFYING / CLASSIFYING
EXPERTS
 Facilitate the process of finding the right people
whom we may ask a specific question and who will
answer that question for us.
 Finding the experts is complexity task due to
diversity of the expertise and the tacit knowledge
 Effective communication of tacit knowledge requires
extensive personal contact and trust which is not
feasible all the time.
 Personal social networks can be falsely built.
 So Semantic based social network is promising
solution

7. SEMANTIC SOCIAL NETWORK
BASED EXPERT
RECOMMENDATION SYSTEM

8. THE PROPOSED FRAMEWORK

9.  Constructing an expert’s profile
• Textual profile is constructed for each individual expert
contains expertise and experience , collected from different
online sources on the web
 Semantic enrichment of an expert’s profile
1. Extracting background knowledge from Wikipedia titles
Si,j denotes the semantic similarity (redirected links ) between two
Wikipedia titles (concepts).
Semantic Kernel S =.

10. A) Concepts match mapping scheme
Concept match scheme maps the text document profiles to the
Wikipedia concepts directly
2) INTEGRATING BACKGROUND KNOWLEDGE INTO
EXPERTS’ PROFILES
Semantic based Document Concept
similarity matrix (SDC)
Linear combination of document-concept similarity matrix (DC) and
semantic kernel (S) produces semantic document-concept similarity
matrix (SDC)
tf/idf

11. B) INFORMATION ITEM RELATEDNESS MAPPING
SCHEME
Information items are used as features to connect text document
profiles to Wikipedia concepts
Linear combination of Information item-word similarity matrix (IW)
and word-concept similarity matrix (WC) produces information item-
concept Similarity Matrix (IC)
SDC I ,j =S I ,j * (DI I ,j * IC I
,j )

12. 3) CONSTRUCTING THE SEMANTIC SOCIAL
NETWORK OF EXPERTS
4) DETECTING EXPERT COMMUNITIES AND
REPRESENTATIVE
 2 mode network is constructed from SDC matrix . 1
mode network of experts profiles generated by folding
method
 Detected expert communities using clustering
algorithms
Aim : Maximize within cluster similarity
(homogeneity) and maximize clusters dissimilarity
(separateness)
 Cluster representative is selected using the
eigenvector centrality measure.5) BUILDING EXPERT RECOMMENDATION
SYSTEM
 Prediction is made to recommend an expert or
community that has required expertise to fulfill the user’s
specific information need.

13. A CASE STUDY
 315 computer science academic experts are chosen
from 16th
ACM conference’s program committee
 62 keywords under conference topics are listed (on
data mining and knowledge recovery )
 Main goal : assess the effectiveness of proposed
model in assigning conference papers to concerned
experts for review process
1. From the user’s perspective, she/he is provided
with a group of experts who can help the user with
her/his information needs.
2. From the expert’s perspective she/he has been
assigned to work on relevant information items that
fall under her/his expertise and interests.

14. AN EXAMPLE OF A EXPERT PROFILE

15. EXAMPLE OF TREE STRUCTURE OF
WIKIPEDIA ARTICLES

16. CLUSTERING ANALYSIS
 Weka 7.3 tool
 ORA tool
 Optimal :12 cluster

17. PERFORMANCE ANALYSIS

18. THE IMPACT OF THE SOCIAL
STRUCTURE OF EXPERTS’
RELATIONSHIPS

19. THE PERFORMANCE OF THE
RECOMMENDER SYSTEM

20. CONCLUSIONS
 This paper proposed a hybrid method for an expert
recommendation system that integrates the characteristics
of content-based recommendation algorithms into a social
network-based collaborative filtering system.
 Semantic-based social network, communities are detected
by clustering analysis and representative of communities
can be detected by applying SNA measures.
 Recommendations are made based on the relevancy of an
information item, for which a user is looking for experts, to
the knowledge carried by representatives of groups.
 The proposed framework was tested in a typical
application domain with a real data set.
 Experimental results show that not only does the presence
of social components has a positive impact in increasing
the accuracy of recommendation, but also discovering
hidden relations among actors influence the accuracy of
predictions in social communities.