Multi-tiered approach to E-mail prioritization

• US 9,152,953 B2
• Filed: 06/15/2012
• Issued: 10/06/2015
• Est. Priority Date: 02/10/2012
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Lai, Jennifer C., Lu, Jie, Pan, Shimei, Wen, Zhen
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Smith, Paulinho E
• Application Number:
  US13/525,173
• Publication Number:
  US 20130212047A1
• Time in Patent Office: 1,208 Days
• US Class Current: 1/1
• CPC Class Codes: G06N 5/00 Computing arrangements using knowledge-based models
• CPC Class Codes: G06Q 10/107 Computer-aided management of electronic mailing [e-mailing]

Abstract

An apparatus for automating a prioritization of an incoming message, including a batch learning module that generates a global classifier based on training data that is input to the batch learning module. A feedback learning module that generates a user-specific classifier based on a plurality of feedback instances. A feature extraction module that receives the incoming message and a topic-based user model, infers a topic of the incoming message based on the topic-based user model, and computes a plurality of contextual features of the incoming message. A classification module that dynamically determines a priority classification strategy for assigning a priority level to the incoming message based on the plurality of contextual features of the incoming message and a weighted combination of the global classifier and the user-specific classifier, and classifies the incoming message based on the priority classification strategy.

Claims

1. An apparatus, comprising:

an input to receive an incoming message;

at least one processor; and

a memory tangibly embodying a set of instructions for automating a prioritization of the incoming message, the instructions comprising;

a batch learning module that trains a global classifier using contextual features computed from a plurality of e-mail messages and a priority level assigned to each of the plurality of e-mail messages;

a feedback learning module that dynamically trains a user-specific classifier based on a plurality of feedback instances provided by a user regarding a priority level of previous incoming e-mail messages to the user;

a classification module that;

dynamically assesses a message-specific quality of the user-specific classifier by matching contextual features of the incoming message against contextual features of the plurality of feedback instances provided by the user;

selects a priority classification strategy from a plurality of priority classification strategies based on the assessed quality of the user-specific classifier, the priority classification strategy using at least one of the global classifier and the user-specific classifier;

classifies the incoming message based on the selected priority classification strategy.

2. The apparatus of claim 1, wherein the plurality of priority classification strategies comprises a dynamic linear combination scheme with instance matching that, based on instance matching between of the contextual features of the incoming message and contextual features of the plurality of feedback instances, the dynamic linear combination scheme with instance matching comprising:

assessing a quality of the user-specific classifier; and

based on the assessed quality of the user-specific classifier, assigning a weight to each of the global classifier and the user-specific classifier for a linear combination thereof.

3. The apparatus of claim 1, wherein the plurality of priority classification strategies comprises a dynamic linear combination scheme with instance matching, and

wherein, when the incoming message and a feedback instance of the plurality of feedback instances have at least one of a same sender and subject, the dynamic linear combination scheme with instance matching assigns a same priority to the incoming message as a priority assigned to the feedback instance having at least one of the same sender and subject.

4. The apparatus of claim 3, wherein, when the incoming message does not have at least one of the same sender and subject as any of the plurality of feedback instances, the dynamic linear combination scheme with instance matching assigns a weight to each of the global classifier and the user-specific classifier for a linear combination thereof.

5. The apparatus of claim 1, wherein the plurality of contextual features comprises a people-centric contextual feature and a message-centric contextual feature.

6. The apparatus of claim 1, wherein the user-specific classifier is only updated based on explicit feedback.

7. The apparatus of claim 1, wherein the user-specific classifier is updated based on explicit feedback and implicit feedback.

8. The apparatus of claim 1, wherein the plurality of feedback instances includes explicit feedback instances, each explicit feedback instance indicating a priority ranking of a different message.

9. The apparatus of claim 1, further comprising:

a feature extraction module that receives the incoming message and a topic-based user model, infers a topic of the incoming message based on the topic-based user model, and computes the contextual features of the incoming message,

wherein the feature extraction module computes the plurality of contextual features of the incoming message based on the topic of the incoming message having been inferred by the feature extraction module.

10. The apparatus of claim 9, wherein the feature extraction module computes a contextual feature of the plurality of contextual features of the incoming message based on a combination of a plurality of factors including:

a percentage of messages previously received by the input that have a substantially similar topic as the topic of the incoming message inferred by the feature extraction module;

a percentage of the messages previously received having the substantially similar topic which are determined to have been read; and

a percentage of the messages previously received having the substantially similar topic which are determined to have been at least one of forwarded, replied, saved, and flagged.

11. The apparatus of claim 9, wherein the feature extraction modules computes the contextual features of the incoming message based on the topic of the incoming message having been inferred by the feature extraction module.

12. The apparatus of claim 9, wherein the plurality of contextual features comprises a message centric feature that is computed based on a percentage of received messages comprising a substantially similar topic as the topic of the incoming message having been inferred by the feature extraction module.

13. A computer system comprising:

a memory tangibly embodying a set of instructions for automating a prioritization of an incoming message, the instructions causing the computer system to comprise;

a topic-based user model for a user, to encode an interaction history and a topic model that the user has with the user'"'"'s e-mail contacts, and relationship data with the user and the user'"'"'s e-mail contacts;

a plurality of contextual features of a plurality of e-mail messages received by the user, based on a content of the messages and the interaction history, the topic model, and the relationship data;

a batch learning module that trains a global classifier using the plurality of contextual features computed from the plurality of e-mail messages and a priority level assigned to each of the plurality of e-mail messages;

a feedback learning module that dynamically trains a user-specific classifier with a plurality of feedback instances provided by a user regarding a priority level of previous incoming e-mail messages to the user; and

a classification module that;

dynamically assesses a message-specific quality of the user-specific classifier by matching contextual features of an incoming message against contextual features of the plurality of feedback instances provided by the user;

selects a priority classification strategy from a plurality of priority classification strategies based on the assessed quality of the user-specific classifier, the priority classification strategy using at least one of the global classifier and the user-specific classifier; and

classifies the incoming message based on the selected priority classification strategy.

14. An apparatus, comprising:

an input to receive an incoming message;

at least one processor; and

a memory tangibly embodying a set of instructions for automating a prioritization of the incoming message, the instructions causing the apparatus to comprise;

a topic-based user model for a user, to encode an interaction history and a topic model that the user has with the user'"'"'s e-mail contacts, and relationship data with the user and the user'"'"'s e-mail contacts;

a plurality of contextual features of a plurality of e-mail messages received by the user, based on a content of the messages and the interaction history, the topic model, and the relationship data;

a batch learning module which trains a global classifier using contextual features computed from a plurality of e-mail messages and a priority level assigned to each of the plurality of e-mail messages;

a feedback learning module which dynamically trains a user-specific classifier based on a plurality of feedback instances provided by a user regarding a priority level of previous incoming e-mail messages to the user; and

a classification module that;

dynamically assesses a message-specific quality of the user-specific classifier by matching contextual features of the incoming message against contextual features of the plurality of feedback instances provided by the user;

selects a priority classification strategy from a plurality of priority classification strategies based on the assessed quality of the user-specific classifier, the priority classification strategy using at least one of the global classifier and the user-specific classifier; and

classifies the incoming message based on the selected priority classification strategy.

15. The apparatus according to claim 14,

wherein, when the matching is above a predetermined threshold, the user-specific classifier is assigned a weight that is greater than a weight assigned to the global classifier.