Social Media Recommendation based on People and Tags

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.