Electronic communications triage using recipient's historical behavioral and feedback
Triaging electronic communications in a computing system environment can mitigate issues related to large volumes of incoming electronic communications. This can include an analysis of user-specific electronic communication data and associated behaviors to predict which communications a user is likely to deem important or unimportant. Client-side application features are exposed based on the evaluation of communication importance to enable the user to process arbitrarily large volumes of incoming communications.
1. A method for triaging electronic communications in a computing system environment, the method comprising:
training a default model at a computing device to personalize a recipient-specific model for a recipient, wherein the default model is formed from a plurality of weighted factors adjusted against a sample of users having common characteristics with the recipient, and the recipient-specific model is formed from the default model that is modified using the recipient's historical behavioral and feedback information;
intercepting an item addressed to the recipient at the computing device;
extracting a plurality of item features associated with the item at the computing device;
retrieving the recipient-specific model, wherein the recipient-specific model comprises the plurality of weighted factors associated to the plurality of extracted item features;
applying an importance classification model to the plurality of extracted item features, including forming a combination of the plurality of weighted factors, by calculating an importance weight as a probability range of threshold values;
generating a predicted item importance based on the combination of the plurality of weighted factors; and
enabling at least one application feature associated with the item for the recipient based on the predicted item importance.
2. The method of claim 1 , wherein the common characteristics comprise one or more of a common vocation and common interest.
3. The method of claim 1 , further comprising adjusting the plurality of weighted factors based on the recipient's historical behavioral and feedback information.
4. The method of claim 1 , further comprising continuing training of the default model to personalize the recipient-specific model by acquiring recipient behavior associated with the item.
5. The method of claim 1 , further comprising continuing training of the default model to personalize the recipient-specific model by acquiring recipient feedback associated with the item.
6. The method of claim 1 , further comprising continuing training of the default model to personalize the recipient-specific model by acquiring recipient customization, the recipient customization comprising one or more of an inference correction, processing rule definition, threshold definition and importance granularity.
7. The method of claim 1 , further comprising continuing training of the default model to personalize the recipient-specific model by periodically acquiring recipient behavior associated with the item.
8. The method of claim 1 , further comprising the predicted item importance designating relative importance of the item.
9. The method of claim 8 , further comprising periodically acquiring recipient behavior associated with the item for a predetermined time period to evaluate correctness of the predicted item importance.
10. The method of claim 9 , further comprising adjusting at least one of: the plurality of weighted factors; and the predicted item importance based on the acquired recipient behavior.
11. The method of claim 8 , further comprising periodically acquiring recipient feedback associated with the item for a predetermined time period to evaluate correctness of the predicted item importance.
12. The method of claim 11 , further comprising adjusting at least one of: the plurality of weighted factors; and the predicted item importance based on the recipient feedback.
13. The method of claim 1 , wherein the item includes a communication comprising one or more of an e-mail message, a voicemail message, a calendar message, an instant message, a web-based message and a social collaboration message.
14. The method of claim 1 , wherein the extracted item features includes at least one of a directly observed item characteristic and an inferred item characteristic.
15. The method of claim 1 , further comprising enabling the application feature selected from a group including: an emphasizing feature for highlighting key content of the item; a display feature for providing a quick view of the item; a notification feature for providing temporary view of the item and including information related to derived importance of the item; an auto-prioritize feature for providing an importance sorted view of the item and other items; an age-out feature for providing an action to the item after a time period; a synopsis feature for providing synopsis of content of the item; and a dashboard feature for providing a consolidated view of important communications across different data sources.
16. A computing device, comprising:
a processing unit;
a system memory connected to the processing unit, the system memory including instructions that, when executed by the processing unit, cause the processing unit to implement a training module configured for hierarchical training of a user model for triaging electronic communications in a computing system environment, the training module being configured to:
generate a set of default inferences for a user based on the prototypical user model, wherein a default inference comprises an item attribute, an attribute value, an attribute weight, and an attribute confidence;
acquire user-specific information to personalize the set of default inferences to the user including: retrieval of user-specific historical behavioral and feedback information, and retrieval of user-specific behavioral and feedback information in response to receipt of an item;
update the set of default inferences with the user-specific information to form a personalized set of inferences for application to an item triage model; and
enable at least one application feature associated with the user for exposing a predicted item importance, the predicted item importance being generated from an importance classification model utilized to calculate an importance weight as a probability range of threshold values based on a combination of a plurality of weighted factors.
17. The computing device of claim 16 , wherein an item comprises an electronic communication, and wherein the item attribute comprises a characteristic of a particular element of the communication, the attribute value comprises a specific instance of the item attribute, the attribute weight comprises a scaled value denoting importance of the attribute value, and the attribute confidence comprises a value designating confidence associated with the attribute weight.
18. The computing device of claim 16 , wherein the prototypical model comprises a plurality of weighted factors adjusted against a sample of users having characteristics common with the user, the common characteristics comprising one or more of a common vocation and common interest.
19. The computing device of claim 16 , wherein retrieval of the user-specific behavioral and feedback information in response to receipt of an item comprises periodic data acquisition to continuously adjust the personalized set of inferences.
20. A physical computer readable storage medium storing computer-executable instructions that, when executed by a computing device, cause the computing device to perform steps comprising:
training a default model at a computing device to personalize a recipient-specific model for a recipient, wherein the default model is formed from a plurality of weighted factors adjusted against a sample of users having common characteristics with the recipient, the common characteristics selected from a group including: common vocation, and common interest, and the recipient-specific model is formed from the default model that is modified using the recipient's historical behavioral and feedback information;
intercepting an item addressed to the recipient at the computing device, wherein the item selected from a group including: an e-mail message, a calendar message, an instant message, a web-based message, and a social collaboration message;
extracting a plurality of item features associated with the item at the computing device, wherein the item features include a characteristic of the item selected from a group including: an item sender characteristic, an item recipient characteristic, a conversation characteristic, and an attachment characteristic;
retrieving the recipient-specific model, wherein the recipient-specific model comprises the plurality of weighted factors associated to the plurality of extracted item features;
applying an importance classification model to the plurality of extracted item features, including forming a combination of the plurality of weighted factors, by calculating an importance weight as a probability range of threshold values;
generating a predicted item importance based on the combination of the plurality of weighted factors, wherein the predicted item importance designating the item as one of: important, and unimportant;
enabling at least one application feature associated with the item for the recipient based on the predicted item importance selected from a group including: an emphasizing feature for highlighting key content of the item; and display feature for providing a quick view of the item; and a notification feature for providing temporary view of the item; and
periodically acquiring recipient behavior and feedback associated with the item for a predetermined time period for continuing training of the default model to personalize the recipient-specific model.