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 site Amazon.com, Ebay Know new friends Friendster, Orkut Personal model v.s.User model Catergoary-based representation

Example: Orkut passions Common interest

Social Network Profile Domain-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 profiles Exact out a normalized representation Augment the normalized profile with metadata to facilitate connection-making Apply 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 analysis Landauer, 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 clique When 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 recommendations Effect of using spreading activation rather than PMI scores

Evaluation and Performance

Discussion Tradeoff: Fixed ontology versus open-ended input Socially costly recommendation Implicit and privacy --> no cost Make sure for conscious rating --> some cost Users 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