Social Network Collaborative Filtering Research Meeting

1. Babar Tareen 2009. 02. 27. Social Network Collaborative FilteringResearch Meeting

2. 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

3. 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

4. Socialy Collaborative Filtering [Cisco White Paper 2008] Based on Socially Relevant Gestures (SRG)

5. 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

6. 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.