InterestMap- Harvesting Social Network

Profiles for Recommendation

Hugo Liu (MIT Media lab)

Pattie Maes (MIT Media lab)

Speaker: Huang, Yi-Ching

Outline

Introduction

Social Network Profiles

The InterestMap Approach

Recommendations by using InterestMap

Evaluation and Performance

Discussion

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

Example: Orkutpassions

Common interest

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

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

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

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

Map of Interests and Identities

Latent semantic analysisLandauer, Foltz & Laham, 1998

Pointwise mutual information (PMI)

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

Network Ontology

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

Evaluation and Performance

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

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