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, wherein the item includes a communication selected from a group of different data sources; 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; generating a predicted item importance based on the combination of the plurality of weighted factors, wherein the predicted item importance is calculated as a probability ranging from 0 to 1, and wherein threshold ranges designate items as at least one of important and unimportant; and 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, a display feature for providing a quick view of the item, and a notification feature for providing temporary view of the item, wherein the method further comprises providing a view of only important items across the different data sources, wherein the view further exposes a set of user inferences (805a, 805b, 805c) as to why a certain item is determined as important, wherein the view exposes an inference feedback button to enable the recipient to provide feedback designating an inference as incorrect, and wherein the feedback updates the importance of the item, and wherein the feedback updates the associated recipient-specific model. 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, wherein the item includes a communication selected from a group of different data sources; 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; generating a predicted item importance based on the combination of the plurality of weighted factors, wherein the predicted item importance is calculated as a probability ranging from 0 to 1, and wherein threshold ranges designate items as at least one of important and unimportant; and 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, a display feature for providing a quick view of the item, and a notification feature for providing temporary view of the item, wherein the method further comprises providing a view of only important items across the different data sources, wherein the view further exposes a set of user inferences (805a, 805b, 805c) as to why a certain item is determined as important, wherein the view exposes an inference feedback button to enable the recipient to provide feedback designating an inference as incorrect, and wherein the feedback updates the importance of the item, and wherein the feedback updates the associated recipient-specific model.