1. Center for E-Business Technology Seoul National University Seoul, Korea Social Network Collaborative Filtering Research Meeting Babar Tareen 2009. 02. 27.

2. Copyright  2008 by CEBT Interestmap [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 2

3. 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 3

4. Copyright  2008 by CEBT Socialy Collaborative Filtering [Cisco White Paper 2008]  Based on Socially Relevant Gestures (SRG) 4

5. 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 5

6. 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. 6