A SEMANTIC SOCIAL NETWORK-BASED EXPERT RECOMMENDER SYSTEM

Elnaz Davoodi, Keivan Kianmehr, Mohsen Afsharchi

Negotiated Studies Presentation on

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

OUTLINE

Abstract

Introduction to Recommendation

System

The proposed framework

A case study

Performance analysis

Conclusions

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.

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

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

SEMANTIC SOCIAL NETWORK BASED EXPERT RECOMMENDATION SYSTEM

THE PROPOSED FRAMEWORK

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 profile1. Extracting background knowledge from Wikipedia

titles

Si,j denotes the semantic similarity (redirected links ) between two Wikipedia titles (concepts).

Semantic Kernel S =.

A) Concepts match mapping schemeConcept 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

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 )

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 algorithmsAim : 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.

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.

AN EXAMPLE OF A EXPERT PROFILE

EXAMPLE OF TREE STRUCTURE OF WIKIPEDIA ARTICLES

CLUSTERING ANALYSIS

Weka 7.3 tool ORA tool Optimal :12 cluster

PERFORMANCE ANALYSIS

THE IMPACT OF THE SOCIAL STRUCTURE OF EXPERTS’ RELATIONSHIPS

THE PERFORMANCE OF THE RECOMMENDER SYSTEM

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.