Center for E-Business TechnologySeoul National University

Seoul, Korea

Social Network Collaborative FilteringResearch Meeting

Babar Tareen

2009. 02. 27.

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

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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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