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InterestMap - Harvesting Social Network Profiles for Recommendation

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 E valuation and Performance

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InterestMap - Harvesting Social Network Profiles for Recommendation

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  1. InterestMap- Harvesting Social Network Profiles for Recommendation Hugo Liu (MIT Media lab) Pattie Maes (MIT Media lab) Speaker: Huang, Yi-Ching

  2. Outline • Introduction • Social Network Profiles • The InterestMap Approach • Recommendations by using InterestMap • Evaluation and Performance • Discussion

  3. 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

  4. Example: Orkut passions Common interest

  5. 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

  6. 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

  7. 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

  8. 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

  9. Map of Interests and Identities • Latent semantic analysis • Landauer, Foltz & Laham, 1998 • Pointwise mutual information (PMI)

  10. 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

  11. Network Ontology

  12. 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

  13. Evaluation and Performance

  14. 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

  15. 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

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