InterestMap - Harvesting Social Network Profiles for Recommendation
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Transcript of InterestMap - Harvesting Social Network Profiles for Recommendation
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InterestMap- Harvesting Social Network
Profiles for Recommendation
Hugo Liu (MIT Media lab)
Pattie Maes (MIT Media lab)
Speaker: Huang, Yi-Ching
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Outline
Introduction
Social Network Profiles
The InterestMap Approach
Recommendations by using InterestMap
Evaluation and Performance
Discussion
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Introduction
Recommendation Systems become more central to people’s lives
E-commerce siteAmazon.com, Ebay
Know new friendsFriendster, Orkut
Personal model v.s.User model
Catergoary-based representation
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Example: Orkutpassions
Common interest
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Social Network ProfileDomain-independent user models
Friendster, Orkut, MySpace
Distinguish passions from other category into ontology identity descriptors
Items map into their respective ontology of interest descriptors
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InterestMap Approach
How to build InterestMap?Steps:
Mine social network profilesExact out a normalized representation Augment the normalized profile with metadata to facilitate connection-makingApply machine learning technique to learn the semantic relatedness weights between every pair of descriptors
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Normalized Representation
Mine 100,000 personal profiles“passions” and common interest categories
Use natural language procession
Newly segmented list contain casually-stated keyphrase referring to different things
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Normalized Representation
21,000 interest descriptor and 1,000 identity descriptor
Use ODP(Open Directory Project), TV tome, Wikipedia, All Music Guide …etc
Identity descriptor: use ODP
Increase the chances that the learning algorithm will discover latent semantic connection
Discount 0f 0.5
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Map of Interests and Identities
Latent semantic analysisLandauer, Foltz & Laham, 1998
Pointwise mutual information (PMI)
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Network Ontology
Features:Identity hubs: identity descriptor node
Behave as “hubs” in the network
Link to interest descriptor node
Appear frequency: Identity descriptor : interest descriptor = 18 : 1
Taste cliqueWhen cohesion of clique is strong, taste clique behave much like a singular identity hub, in its impact on network flow
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Network Ontology
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Recommendations
Use InterestMap
Finding recommendations by spreading activation
Evaluation Features:Impact that identity hubs and taste cliques in the recommendationsEffect of using spreading activation rather than PMI scores
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Evaluation and Performance
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Discussion
Tradeoff:Fixed ontology versus open-ended input
Socially costly recommendationImplicit and privacy --> no costMake sure for conscious rating --> some costUsers list items in their profile --> great cost
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Conclusion
Recommender systems provide some suggestions of things to do and people to meet
General personal model for people behave “in the wild” on the Web
Using cultural and taste model to recommendation