DETERMINING A PRIORITY SCORE OF A COMPUTER SYSTEM ALERT BY USING A MACHINE LEARNING OPERATION
Systems, methods, and software can be used to determine whether a priority score of an alert. In some aspects, a method includes: receiving an alert, wherein the alert comprises activity information and user information; obtaining a set of activity features based on the activity information; obtaining a set of user features based on the user information; and determining a score of the alert based on the set of activity features and the set of user features.
1 . A method, comprising:
receiving an alert, wherein the alert comprises activity information and user information;
obtaining a set of activity features based on the activity information;
obtaining a set of user features based on the user information; and
determining a score of the alert based on the set of activity features and the set of user features.
2 . The method of claim 1 , wherein the determining the score comprises:
determining an activity feature vector based on the set of activity features;
determining a user feature vector based on the set of user features; and
determining the score based on the activity feature vector and the user feature vector.
3 . The method of claim 1 , wherein the obtaining a set of user features comprises:
determining, a user group based on the user information; and
wherein the set of user features comprises a feature of the user group.
4 . The method of claim 1 , wherein the score is determined using machine learning operations.
5 . The method of claim 4 , wherein the machine learning operations comprise processing an activity feature vector by using a first machine learning model and processing a user feature vector by using a second machine learning model.
6 . The method of claim 5 , wherein the score is determined by combining a first output of the first machine learning model and a second output of the second machine learning model.
7 . The method of claim 1 , further comprising: performing a responsive action based on the score.
8 . A computer-readable medium containing instructions which, when executed, cause an electronic device to perform operations comprising:
receiving an alert, wherein the alert comprises activity information and user information;
obtaining a set of activity features based on the activity information;
obtaining a set of user features based on the user information; and
determining a score of the alert based on the set of activity features and the set of user features.
9 . The computer-readable medium of claim 8 , wherein the determining the score comprises:
determining an activity feature vector based on the set of activity features;
determining a user feature vector based on the set of user features; and
determining the score based on the activity feature vector and the user feature vector.
10 . The computer-readable medium of claim 8 , wherein the obtaining a set of user features comprises:
determining, a user group based on the user information; and
wherein the set of user features comprises a feature of the user group.
11 . The computer-readable medium of claim 8 , wherein the score is determined using machine learning operations.
12 . The computer-readable medium of claim 11 , wherein the machine learning operations comprise processing an activity feature vector by using a first machine learning model and processing a user feature vector by using a second machine learning model.
13 . The computer-readable medium of claim 12 , wherein the score is determined by combining a first output of the first machine learning model and a second output of the second machine learning model.
14 . The computer-readable medium of claim 8 , the operations further comprising: performing a responsive action based on the score.
15 . A computer-implemented system, comprising:
one or more computers; and
one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:
receiving an alert, wherein the alert comprises activity information and user information;
obtaining a set of activity features based on the activity information;
obtaining a set of user features based on the user information; and
determining a score of the alert based on the set of activity features and the set of user features.
16 . The computer-implemented system of claim 15 , wherein the determining the score comprises:
determining an activity feature vector based on the set of activity features;
determining a user feature vector based on the set of user features; and
determining the score based on the activity feature vector and the user feature vector.
17 . The computer-implemented system of claim 15 , wherein the obtaining a set of user features comprises:
determining, a user group based on the user information; and
wherein the set of user features comprises a feature of the user group.
18 . The computer-implemented system of claim 15 , wherein the score is determined using machine learning operations.
19 . The computer-implemented system of claim 18 , wherein the machine learning operations comprise processing an activity feature vector by using a first machine learning model and processing a user feature vector by using a second machine learning model.
20 . The computer-implemented system of claim 19 , wherein the score is determined by combining a first output of the first machine learning model and a second output of the second machine learning model.