Social media recommendation based on people and tags
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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. Outline. Introduction Recommender system Social Media Platform Relationship Aggregation User Profile

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Social Media Recommendation based on People and Tags

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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

Speaker: Hsin-Lan, Wang

Date: 2010/10/19


Outline

Outline

  • Introduction

  • Recommender system

    • Social Media Platform

    • Relationship Aggregation

    • User Profile

    • Recommendation Algorithm

    • Recommnder Widget

  • Experiment

  • Conclusion


Introduction

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.


Introduction1

Introduction

  • Study personalized recommendation of social media items within an enterprise social software application suite.

  • The recommender suggests items based on people and tags.


Recommender system

Recommender system

  • Social Media Platform

    • Lotus Connection

      • a social software application suite for organization

      • profiles, activities, bookmarks, blogs, communities, files, and wikis.


Recommender system1

Recommender system

  • Relationship Aggregation

    • SaND

      • Models relationships through data collected across all LC applications.

      • Aggregates any kind of relationships between people, items, and tags.


Recommender system2

Recommender system

  • Relationship Aggregation

    • SaND

      • builds an entity-entity relationship matrix

        • direct relations

        • indirect relations


Recommender system3

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


Recommender system4

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].


Recommender system5

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


Recommender system6

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


Recommender system7

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.


Recommender system8

Recommender system

  • Recommender Widget


Evaluation

Evaluation

  • Tag Profile Survey


Evaluation1

Evaluation

  • Recommended Items Survey

    • PBR: β=1

    • TBR: β=0

    • or-PTBR: β=0.5

    • and-PTBR: β=0.5

    • POPBR: popular item recommendation.


Evaluation2

Evaluation

  • Recommended Items Survey


Evaluation3

Evaluation

  • Recommended Items Survey


Evaluation4

Evaluation

  • Recommended Items Survey


Conclusion

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.


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