1. Social Media Recommendation based on People and Tags Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, Erel Uziel SIGIR ’ 10 Speaker: Hsin-Lan, Wang Date: 2010/10/19

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

3. 3 Introduction Users are flooded with information from feed readers and many other resources. Social media sites are increasingly challenged to attract new users and retain existing ones.

4. 4 Introduction Study personalized recommendation of social media items within an enterprise social software application suite. The recommender suggests items based on people and tags.

5. 5 Recommender system Social Media Platform Lotus Connection a social software application suite for organization profiles, activities, bookmarks, blogs, communities, files, and wikis.

6. 6 Recommender system Relationship Aggregation SaND Models relationships through data collected across all LC applications. Aggregates any kind of relationships between people, items, and tags.

7. 7 Recommender system Relationship Aggregation SaND builds an entity-entity relationship matrix direct relations indirect relations

8. 8 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

9. 9 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].

10. 10 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

11. 11 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

12. 12 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.

13. 13 Recommender system Recommender Widget

14. 14 Evaluation Tag Profile Survey

15. 15 Evaluation Recommended Items Survey PBR: β=1 TBR: β=0 or-PTBR: β=0.5 and-PTBR: β=0.5 POPBR: popular item recommendation.

16. 16 Evaluation Recommended Items Survey

17. 17 Evaluation Recommended Items Survey

18. 18 Evaluation Recommended Items Survey

19. 19 Conclusion Using tags for social media recommendation can be highly beneficial. The combination of directly used tags and incoming tags produces an effective tag-based user profile.