1. Mining Social Network for Personalized Email Prioritization Language Techonology Institute School of Computer Science Carnegie Mellon University Shinjae Yoo, Yiming Yang, Frank Lin, and Il-Chul Moon

2. 2 Outline Problem Description Approaches Experiments Contributions

3. 3 Problem Description Email Overload is severe problem Identifying Importance of email will alleviate email overload Challenges  No access to other people’s emails and labels  Personalized labeling is time consuming  The same message may have different priority labels for different recipients  We want to leverage the sparse training data by using social network of each user Sparse Training Data

4. 4 Outline Problem Description Approaches Social Clustering Social Importance Semi-supervised Importance Propagation Experiments Conclusion and Future Work

5. 5 Social Clustering – Motivation Personal Email Inbox  Lots of unlabeled emails  No privacy issue Observations The sender can be important Some senders are not appeared in the training set at all or very few instances Need generalization of sender  Let’s find similar senders from social network

6. 6 Social Clustering – Contact Network Personal Contact Network  G =(V,E )  All the network is constructed from personal inbox 35412 Agent /Person

7. 11 Social Clustering – Newman Clustering Newman Clustering Algorithm [Newman, 04]  Find social cliques or cohesive social groups  Based on edge betweeness The number of shortest path that go through the edge / the total number of shortest path Drop edges from highest edge betweeness  Hard clustering 1 23 4 56 9 4444 Group AGroup B

8. Social Clustering – Validations 8 Clusters are coherent!

9. Social Clustering – Feature Incorporation Extended Vector Space  text: social network:  combined:  The combined vector space is used as enriched feature set to the email prioritizer 9

10. 10 Social Importance – Motivations Social Importance  A person in the center of a cluster might be more important than others  Betweeness Edge betweeness for Newman Clustering Vertex betweeness  The degree of communication bottleneck from social network  Contact points among the network  Might be important person  We may try other kinds of social importance metrics too

11. 11 Social Importance – Metrics Metrics  Degree (in, out, total) [Wasserman and Faust, 94]  Clique Counts (ClqCnt) [Wasserman and Faust, 94] The number of clique sub-graphs which contain a node v  Betweeness (BetCent) [Freeman, 77]  HITS Authority (Authority) [Kleinberg, 99] λ: the greatest Eigen value r : the Eigen vector  similar to PageRank scores  Neighborhood Connectivity (“Clustering Coefficient”, ClustCoef) [Boykin and Roychowdhury, 05] measure the connectivity among the neighbor of a node v

12. Social Importance – Validations Correlation coefficients with priority levels  12

13. SIP- Motivations Semi-supervised Importance Propagation (SIP) Can we propagate importance labels?  Bi-partite graph, Labels only in Emails 13 Agent /Person Emails 432?? ? ????

14. SIP- Email Network A: Sender to Emails (N x M) B T : Email to Recipients (M x N) x k : k th importance labels for emails(M x 1) y k =Bx k (N x 1) 14 Agent /Person Emails 432?? ? ????

15. SIP - Algorithm Problems of the above propagation  may not be irreducible  is insensitive to (not personalized) Apply Personalized PageRank with  Normalize and column-wise normalize C :C’  15

16. 16 Outline Problem Description Approaches Experiments Contributions

17. Collected Data  25 subjects are recruited from Canegie Mellon University  7 users who submitted more than 200 emails  1 faculty, 2 staffs, 4 students 17 Experiments – Data Collection TrainingTesting time

18. 18 Experiments – Metrics Mean Absolute Error (MAE)   1.0 MAE means on average the prediction is deviated from the truth by one priority level  MAE considers the difference among the errors It ranges from 0 to 4 when we use five importance level 1 vs. 5 and 4 vs. 5  Micro-MAE Pooling the test instances from all users to obtain a joint test set  Macro-MAE Compute each user MAE first and then take the average of per-user MAE

19. Experiments – Setups Features : four subsets  Basic Feature (BF) : from, to, cc, title, body  Newman Clustering (NC)  Social Importance (SI)  Semi-supervised Importance Propagation (SIP) Ten times random shuffling among training data Linear SVM  10 Fold C.V. for parameter tuning  Tuned regularization parameter [10 -3.. 10 3 ] 19

20. Experiments – Results 20

21. 21 Contributions The first study on personalized email prioritization  Using statistical classification and clustering  Based on fine-grained personal judgments with multiple users Enriched representation through personal Social Network  Social Clustering  Social Importance Estimation  Semi-supervised Importance Propagation Fully personalized methodology  Technical development and Evaluation