How many folders do you really need ? Classifying email into a handful of categories.
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How many folders do you really need ? Classifying email into a handful of categories.
- 1. + How Many Folders Do You Really Need? Classifying Email into a Handful of Categories 2014/1/23 (Fri.) Chang Wei-Yuan @ MakeLab Group Meeting Mihajlo Grbovic, Guy Halawi, Zohar Karnin, Yoelle Maarek Yahoo Labs CIKM‘14
- 2. + Outline n Introduction n Method n Discovering Latent Categories n Modeling Data n Training Data n Classification Mechanism n Experiment n Conclusion n Thought 2
- 3. + Outline n Introduction n Method n Discovering Latent Categories n Modeling Data n Training Data n Classification Mechanism n Experiment n Conclusion n Thought 3
- 4. + Introduction n Traditional email classification is still a mostly manual task. 4
- 5. + Introduction n Recently automatic classification has started to appear in some Web mail clients, e.g. Inbox. 5
- 6. + Introduction n The current email traffic is dominated by non-spam machine-generated email. n Social network n Commerce sites n Official institutions 6
- 7. + Introduction n Goal n automatically distinguishing between personal and machine-generated email n classifying messages into latent categories, without requiring users to have defined any folder 7
- 8. + Outline n Introduction n Method n Discovering Latent Categories n Modeling Data n Training Data n Classification Mechanism n Experiment n Conclusion n Thought 8
- 10. + Discovering Latent Categories Latent categories Extracting Features Aggregation Level LDA Training data Classifier Mail raw data Mail testing data raw data
- 11. + Discovering Latent Categories n All messages have the potential to be classified. n by retrieving the most popular folder from users n This paper applied LDA to these "document folders " for finding latent categories. n latent topics would map into "latent categories" 11
- 12. + 12 msg msg msg msg msg msg msg msg msg msg msg msg msg msg
- 13. + 13 msg msg msg msg msg msg msg msg msg msg msg msg msg msg LDA
- 14. + Discovering Latent Categories n Our objective was to train a value of K n each individual and overall set of topics achieve significant coverage n We further examined for K = 6 n good balance between total and individual coverage 14
- 15. + Discovering Latent Categories 15 msg travel %, social % … travel
- 17. + Modeling Data n Original method: Each individual message as a single data point n various features extracted from the message header and body 17
- 18. + Modeling Data n Extracting Features n content features n the message subject and body n address features n sender email address, including the subdomain n behavioral features n sender's and recipient's actions over a given message 18 subject body action time sender address domain msg
- 19. + Modeling Data n Extended method: Aggregating messages at higher levels n address/mail domain level n This paper consider three levels of aggregation. 19 subject body action time address sender domain msg Aggregating : sender level Aggregating : domain level
- 20. + Modeling Data n Aggregation Levels 20 msg: shopping msg: traveling
- 22. + Training Data n labeling techniques n label used as 6 latent categories n we will create a two-stage classifier by msg- level and sender-level 22 subject action … sender domain category msg sender domain category sender
- 23. + Training Data n labeling techniques n label used as 6 latent categories n we will create a two-stage classifier by msg- level and sender-level 23 subject action … sender domain category msg sender domain category sender known by LDA unknown
- 25. + 25 sender human travel social career heuristic-based • Domain : gmail.com, yahoo.com • Sender: first name.last name
- 26. + 26 sender human travel social career automatic voting sender msg msg msg folder1 folder2 folder3 travel 96%, travel 88%, shopping 70%, travel 20 %
- 27. + 27 sender human travel social career automatic voting sender msg msg msg folder1 folder2 folder3 travel travel shopping
- 29. + Classification Mechanism n Offline creation of classified senders table and message-level classier n We use the training set to train a logistic regression model. n For each category we train a separate model in a one-vs-all manner. n The classification process is run performed periodically to account for new senders.
- 30. + Classification Mechanism 35 % sender training data classifier classifier senders table 65 % sender testing data msg training data
- 32. + Classification Mechanism n Online Light-weight classification n The initial classification n hard coded rules designed to quickly classify n This process described requires very few resources and covers 32% of the email traffic.
- 33. + Classification Mechanism n Online Sender-based classification n The second phase in our cascade classification n looking for the sender with known categories n using senders table n The amount of traffic that is not covered by this phase is roughly 8%.
- 34. + Classification Mechanism n Online Heavy-weight classification n As only 8% of the traffic end up in this last phase n We can afford slightly heavier computations to classifier. n use all relevant feature, pertaining to the message body, subject line and sender name
- 40. + Outline n Introduction n Method n Discovering Latent Categories n Modeling Data n Training Data n Classification Mechanism n Experiment n Conclusion n Thought 40
- 41. + Experiment n This paper estimated the actual volume of machine-generated messages on a very large Yahoo mail dataset. n This dataset built for the purpose of this work n 6 months of email traffic n more than 500 billion messages. 41
- 42. + Experiment n 5 sender based classifiers for machine latent categories n Shopping, Financial, Travel, Career and Social n 1 sender-based machine for human classifier.
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- 46. + Outline n Introduction n Method n Discovering Latent Categories n Modeling Data n Training Data n Classification Mechanism n Experiment n Conclusion n Thought 46
- 47. + Conclusion n We presented here a Web-scale categorization approach. n offline learning n online classification n Discovered latent categories. n Discriminated human and machine- generated email. n Building a scalable online system can be applied in Web mail.
- 48. + Future Work n Discussing how categories should be exposed to users.
- 49. + Outline n Introduction n Method n Discovering Latent Categories n Modeling Data n Training Data n Classification Mechanism n Experiment n Conclusion n Thought 49
- 50. + Thought n Extended multiclass classification with multi-label. 50
- 54. + Thanks for listening. 2014 / 01 / 23 (Tue.) @ MakeLab Group Meeting v123582@gmail.com