Center for E-Business TechnologySeoul National University
Seoul, Korea
Social Network Collaborative FilteringResearch Meeting
Babar Tareen2009. 02. 27.
Copyright 2008 by CEBT
Intrestmap [2005] Uses Social Network Profile details like Hobbies and
Passions for Content Recommendation Book reading, adventure, pets, etc
Uses NLP to map content to ontology of concepts Build a Interest map by using Point Mutual Information
between different user profiles
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Copyright 2008 by CEBT
Semantic Social Collaborative Filtering [2008] Focuses on Information Retrieval User managed collections
Conceptually similar to online bookmarks Every collection has quality level User expertise on a given topic can be computed with
PageRank algorithm Quality of a collection corresponds to the expertise level
of the owner Access Control
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Copyright 2008 by CEBT
Socialy Collaborative Filtering [Cisco White Paper 2008]
Based on Socially Relevant Gestures (SRG)
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Copyright 2008 by CEBT
Social Network Collaborative Filtering [2007] Uses Social network as similar user set for Collaborative Filtering
Only use people from Social network as recommenders Used Amazon.com data about purchases and users’ friends Drawbacks: For very specific areas of interest, only using social
network users might not be very good Ex: Buying a book about Ontologies
We can try to give more weight to users who are in Social Network but use large number of user for CF
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Copyright 2008 by CEBT
References H. Liu and P. Maes, “Interestmap: Harvesting social
network profiles for recommendations,” In Proceedings of the Beyond Personalization 2005 Workshop, 2005.
Sebastian Ryszard Kruk and Stefan Decker, “Semantic Social Collaborative Filtering with FOAFRealm,” Apr. 2008.
R. Zheng, F. Provost, and A. Ghose, “Social Network Collaborative Filtering,” 2007.
“Socially Collaborative Filtering: Give Users Relevant Content,” 2008.
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