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Social Media Recommendation based on People and Tags

Social Media Recommendation based on People and Tags. Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, Erel Uziel SIGIR ’ 10. Fırat Onur Alsaran. Outline. Introduction Recommender system Recomm e nder Widget Social Media Platform Relationship Aggregation User Profile

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Social Media Recommendation based on People and Tags

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  1. Social Media Recommendation based on People and Tags Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, Erel Uziel SIGIR’10 Fırat Onur Alsaran

  2. Outline • Introduction • Recommender system • Recommender Widget • Social Media Platform • Relationship Aggregation • User Profile • Recommendation Algorithm • Experiments • Conclusion

  3. Introduction

  4. Introduction • Users are flooded with content • How to judge the validity of so much content? • As social media grows larger everyday, these web sites are increasingly challenged to attract new users and retain existing ones. • Contribution: Study personalized recommendation of social media items

  5. Recommender system • Recommender Widget

  6. Recommender System • Lotus Connections: • A social software application suite • profiles, activities, bookmarks, blogs, communities, files, and wikis. • Recommendation platform forthesystem

  7. Recommender system • Relationship Aggregation • SaND • Models relationships through data collected across all LC applications. • Aggregates any kind of relationships between people, items, and tags. • For each user, weighted lists of PEOPLE, ITEMS and TAGS are extracted

  8. Recommender system • Relationship Aggregation • SaND • builds an entity-entity relationship matrix • direct relations • indirect relations

  9. Recommender system • User Profile • P(u): an input to the recommender engine once the user u logs into the system. • N(u): 30 related people • T(u): 30 related tags

  10. Recommender system • User Profile • Person-person relations • Aggregate direct and indirect people-people relations into a single person-person relationship. • Each direct relation adds a sore of 1. • Each indirect relation adds a score in the range of (0,1].

  11. Recommender system • User Profile • User-tag relations • used tags • direct relation based on tags the user has used • incoming tags • direct relation based on tags applied on the user • indirect tags • indirect relation based on tags applied on items related on the user

  12. Recommender system • Tag Profile Survey – participants are asked to evaluate tags as indicators of topic of interest • Combination of used and incoming tags is the best indicator to generate T(U) from SaND system

  13. Recommender system • Recommendation Algorithm • d(i): number of days since the creation date of i • w(u,v) and w(u,t): relationship strengths of u to user v and tag t • w(v,i) and w(t,i): relationship strengths between v and t, respectively, to item i

  14. Recommender system • Recommendation Algorithm • User-item relation: authorship (0.6), membership (0.4), commenting (0.3), and tagging (0.3) • Tag-item relation: number of users who applied the tag on the item, normalized by the overall popularity of the tag.

  15. Evaluation • 5 recommenders • PBR: β=1 • TBR: β=0 • or-PTBR: β=0.5 • and-PTBR: β=0.5 • POPBR: popular item recommendation. • Each participant is assigned to one recommender

  16. Evaluation • Recommended Items Survey

  17. Evaluation • Recommended Items Survey

  18. Conclusion • The combination of directly used tags and incoming tags produces an effective tag-based user profile. • Using tags for social media recommendation can be highly beneficial. • Combining tag and person based recommendations perform better. • Future Work: • Large scale evaluation • Computationally intensive algorithm may be used.

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