Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Structural Diversity in Social
Recommender Systems
Mitul Tiwari
Joint work with
Xinyi (Lisa) Huang and Sam Shah
LinkedIn
2
Who am I
3
Outline
• Introduction
• Motivation
• Goal
• Structural Diversity in Recommendation
• Structural Diversity and User Enga...
4
Introduction
• Social Networks : important for
• Sharing and Discovery
• Communication
• Networking
• Online Social Netw...
5
Introduction: Network is Important
6
Introduction: People You May Know
7
Introduction: Goal
• How does structural diversity of network plays a role in
• Recommendations of people?
• User Engage...
8
Outline
• Introduction
• Motivation
• Goal
• Structural Diversity in Recommendation
• Structural Diversity and User Enga...
9
Structural Diversity in PYMK Recommendations
• Members in recommendation set mapped to a graph G
• Vertices represent me...
10
Structural Diversity in Recommendations
• A connected component
• any pair of vertices are connected by a path or an is...
11
Structural Diversity in Recommendations
• Invitation rate increases as the number of components decreases
12
Structural Diversity in Recommendations
• Members in recommendation set mapped to a graph G
• A triangle in graph G
• S...
13
Structural Diversity in Recommendations
• Invitation rate increases as the number of triangles increases
14
Structural Diversity in Recommendations
• Members in recommendation set mapped to a graph G
• Local node degree
• Numbe...
15
Recommendations
• Invitation rate increases as the avg local node degree
increases
16
Structural Diversity in Recommendations
• Less diverse the result set, the higher the invitation rates
• Explanation: A...
17
Outline
• Introduction
• Motivation
• Goal
• Structural Diversity in Recommendation
• Structural Diversity and User Eng...
18
Structural Diversity and User Engagement
• A member is engaged if visits the site multiple times a week
• How does enga...
19
Structural Diversity and User Engagement
• K-core decomposition of a graph
• Repeatedly remove nodes with less than K n...
20
Structural Diversity and User Engagement
• Higher engagement with higher number K-core components
21
Structural Diversity and User Engagement
• Higher engagement with higher number K-core components
22
Concluding remarks
• Lower structural diversity among recommendation set results
in a higher invitation rate
• Differen...
23
Related Work
24
Acknowledgement
• http://data.linkedin.com
• We are hiring!
• Contact: mtiwari[at]linkedin.com
25
Questions?
Upcoming SlideShare
Loading in …5
×

Structural Diversity in Social Recommender Systems

898 views

Published on

Recsys RSWeb 2013 paper presentation slides. Paper can be found here: Structural Diversity in Social Recommender Systems

Published in: Internet
  • Be the first to comment

  • Be the first to like this

Structural Diversity in Social Recommender Systems

  1. 1. Structural Diversity in Social Recommender Systems Mitul Tiwari Joint work with Xinyi (Lisa) Huang and Sam Shah LinkedIn
  2. 2. 2 Who am I
  3. 3. 3 Outline • Introduction • Motivation • Goal • Structural Diversity in Recommendation • Structural Diversity and User Engagement • Conclusion
  4. 4. 4 Introduction • Social Networks : important for • Sharing and Discovery • Communication • Networking • Online Social Networks are partially observed • Link Prediction and Recommending entities are important
  5. 5. 5 Introduction: Network is Important
  6. 6. 6 Introduction: People You May Know
  7. 7. 7 Introduction: Goal • How does structural diversity of network plays a role in • Recommendations of people? • User Engagement on the site?
  8. 8. 8 Outline • Introduction • Motivation • Goal • Structural Diversity in Recommendation • Structural Diversity and User Engagement • Conclusion
  9. 9. 9 Structural Diversity in PYMK Recommendations • Members in recommendation set mapped to a graph G • Vertices represent members in the recommendation set • Edges are the connections between those members on LinkedIn social graph • 3 measures of structural diversity • Number of connected components • Number of triangles • Average local node degree
  10. 10. 10 Structural Diversity in Recommendations • A connected component • any pair of vertices are connected by a path or an isolated vertex • Number of connected components • a measure of structural diversity [Ugander et al. 2012] • Smaller number of components => less structural diversity • Effect on Invitation rate or conversion rate • ratio of the number of invitations sent and size of recommended set
  11. 11. 11 Structural Diversity in Recommendations • Invitation rate increases as the number of components decreases
  12. 12. 12 Structural Diversity in Recommendations • Members in recommendation set mapped to a graph G • A triangle in graph G • Set of three vertices in Graph G s.t. each vertex is connected to other two • More number of triangles => dense graph • More number of triangles => less structural diversity • Effect on Invitation rate or conversion rate
  13. 13. 13 Structural Diversity in Recommendations • Invitation rate increases as the number of triangles increases
  14. 14. 14 Structural Diversity in Recommendations • Members in recommendation set mapped to a graph G • Local node degree • Number of edges incident on the node • Avg local node degree • Average of the local node degree over all nodes • Higher avg local node degree => denser graph • Higher avg local node degree => less structural diversity • Effect on Invitation rate or conversion rate
  15. 15. 15 Recommendations • Invitation rate increases as the avg local node degree increases
  16. 16. 16 Structural Diversity in Recommendations • Less diverse the result set, the higher the invitation rates • Explanation: A member knows one person in a recommendation set of connected members then knows other members in the set
  17. 17. 17 Outline • Introduction • Motivation • Goal • Structural Diversity in Recommendation • Structural Diversity and User Engagement • Conclusion
  18. 18. 18 Structural Diversity and User Engagement • A member is engaged if visits the site multiple times a week • How does engagement depend on the structure of a member’s immediate connection network? • Connections of a member is mapped to a graph • Vertices represent members in the connections set • Edges are the connections between those members on LinkedIn social graph
  19. 19. 19 Structural Diversity and User Engagement • K-core decomposition of a graph • Repeatedly remove nodes with less than K neighbors • Eliminate influence from unimportant nodes • K-components • Connected components in K-core decomposition of the graph
  20. 20. 20 Structural Diversity and User Engagement • Higher engagement with higher number K-core components
  21. 21. 21 Structural Diversity and User Engagement • Higher engagement with higher number K-core components
  22. 22. 22 Concluding remarks • Lower structural diversity among recommendation set results in a higher invitation rate • Different form Facebook data study [Ugander et al. 2012] • Use case is slightly different • Effect of structural diversity on recommender system highly depends on the use • Don’t generalize structural diversity effects on one recommender system to all • Higher structural diversity among member’s connection network results in higher engagement • Similar to Facebook data study
  23. 23. 23 Related Work
  24. 24. 24 Acknowledgement • http://data.linkedin.com • We are hiring! • Contact: mtiwari[at]linkedin.com
  25. 25. 25 Questions?

×