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Negotiated Studies - A semantic social network based expert recommender system
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Transcript of Negotiated Studies - A semantic social network based expert recommender system
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A SEMANTIC SOCIAL NETWORK-BASED EXPERT RECOMMENDER SYSTEM
Elnaz Davoodi, Keivan Kianmehr, Mohsen Afsharchi
Negotiated Studies Presentation on
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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
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OUTLINE
Abstract
Introduction to Recommendation
System
The proposed framework
A case study
Performance analysis
Conclusions
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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.
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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
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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
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SEMANTIC SOCIAL NETWORK BASED EXPERT RECOMMENDATION SYSTEM
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THE PROPOSED FRAMEWORK
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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 =.
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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
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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 )
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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.
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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.
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AN EXAMPLE OF A EXPERT PROFILE
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EXAMPLE OF TREE STRUCTURE OF WIKIPEDIA ARTICLES
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CLUSTERING ANALYSIS
Weka 7.3 tool ORA tool Optimal :12 cluster
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PERFORMANCE ANALYSIS
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THE IMPACT OF THE SOCIAL STRUCTURE OF EXPERTS’ RELATIONSHIPS
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THE PERFORMANCE OF THE RECOMMENDER SYSTEM
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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.
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