Behavior based menu item recommendation and pruning

Patent metadata

Field	Value
Publication number	US-12014026-B2
Application number	US-202318137751-A
Country	US
Kind code	B2
Filing date	Apr 21, 2023
Priority date	Jan 6, 2022
Publication date	Jun 18, 2024
Grant date	Jun 18, 2024

Abstract

Using a set of menu to key process mappings, historical menu usage data for an application is aggregated into aggregated key process usage data. A set of key process association rules, each comprising a consequent key process given a particular antecedent key process, is generated. From the set of key process association rules and a set of ranked menus by frequency of usage within each key process, a set of model menu recommendations is generated. According to an application usage history, a menu frequency ratio, and a confidence value of a modelled next menu, the set of menu recommendations is scored. A scored menu recommendation having a rank below a threshold rank is pruned from a set of menu items of the application ranked according to their scores. The pruned set of scored menu recommendations is presented for selection instead of the set of menu items.

First claim

What is claimed is:

1. A computer-implemented method comprising:
classifying, using a set of menu to key process mappings, a set of menu items of an application into a set of key processes, wherein a key process in the set of key processes comprises at least two menu items;
generating, using historical menu usage data for the application and the set of menu to key process mappings, a set of key process association rules, a key process association rule in the set of key process association rule comprising a consequent key process given a particular antecedent key process;
generating a set of model menu recommendations, a model menu recommendation in the set of model menu recommendations comprising a modelled next menu and a confidence value corresponding to the modelled next menu;
scoring each menu item from the set of menu items, wherein scoring each menu item is based at least in part on a user inclination parameter, a frequency ratio, and the confidence value associated with each menu item;
ranking each menu item based on each menu item scored;
pruning, from the set of menu items of the application, a menu item having a score based rank below a threshold rank, the pruning resulting in a pruned set of scored menu recommendations; and
presenting, for selection instead of the set of menu items, the pruned set of scored menu recommendations.

2. The computer-implemented method of claim 1 , wherein the historical menu usage data comprises a menu selection and a timestamp at which the menu selection occurred.

3. The computer-implemented method of claim 1 , wherein a menu to key process mapping in the set of menu to key process mappings comprises a rule mapping a menu selection to a corresponding key process.

4. The computer-implemented method of claim 1 , further comprising:
aggregating, the historical menu usage data into aggregated key process usage data, wherein the aggregated key process usage data comprises a number of times a first user selected a menu in a key process within a time period.

5. The computer-implemented method of claim 4 , wherein the aggregated key process usage data comprises a number of times any user in a community of users selected a menu in a key process within a time period.

6. The computer-implemented method of claim 1 , wherein the model menu recommendation in the set of model menu recommendations comprises a highest-ranked menu by frequency of usage within the consequent key process.

7. A computer program product for menu item recommendation and pruning, the computer program product comprising:
one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the stored program instructions when executed using a processor causing operations, the operations comprising:
classifying, using a set of menu to key process mappings, a set of menu items of an application into a set of key processes, wherein a key process in the set of key processes comprises at least two menu items;
generating, using historical menu usage data for the application and the set of menu to key process mappings, a set of key process association rules, a key process association rule in the set of key process association rule comprising a consequent key process given a particular antecedent key process;
generating a set of model menu recommendations, a model menu recommendation in the set of model menu recommendations comprising a modelled next menu and a confidence value corresponding to the modelled next menu;
scoring each menu item from the set of menu items, wherein scoring each menu item is based at least in part on a user inclination parameter, a frequency ratio, and the confidence value associated with each menu item;
ranking each menu item based on each menu item scored;
pruning, from the set of menu items of the application, a menu item having a score based rank below a threshold rank, the pruning resulting in a pruned set of scored menu recommendations; and
presenting, for selection instead of the set of menu items, the pruned set of scored menu recommendations.

8. The computer program product of claim 7 , wherein the historical menu usage data comprises a menu selection and a timestamp at which the menu selection occurred.

9. The computer program product of claim 7 ,
wherein a menu to key process mapping in the set of menu to key process mappings comprises a rule mapping a menu selection to a corresponding key process.

10. The computer program product of claim 7 , the operations further comprising:
aggregating, the historical menu usage data into aggregated key process usage data, wherein the aggregated key process usage data comprises a number of times a first user selected a menu in a key process within a time period.

11. The computer program product of claim 10 , wherein the aggregated key process usage data comprises a number of times any user in a community of users selected a menu in a key process within a time period.

12. The computer program product of claim 7 , wherein the model menu recommendation in the set of model menu recommendations comprises a highest-ranked menu by frequency of usage within the consequent key process.

13. The computer program product of claim 7 , wherein the stored program instructions are stored in the at least one of the one or more storage media of a local data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system.

14. The computer program product of claim 7 , wherein the stored program instructions are stored in the at least one of the one or more storage media of a server data processing system, and wherein the stored program instructions are downloaded over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system.

15. The computer program product of claim 7 , wherein the computer program product is provided as a service in a cloud environment.

16. A computer system comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage media, and program instructions stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising:
program instructions to classify, using a set of menu to key process mappings, a set of menu items of an application into a set of key processes, wherein a key process in the set of key processes comprises at least two menu items;
program instructions to generate, using historical menu usage data for the application and the set of menu to key process mappings, a set of key process association rules, a key process association rule in the set of key process association rule comprising a consequent key process given a particular antecedent key process;
program instructions to generate a set of model menu recommendations, a model menu recommendation in the set of model menu recommendations comprising a modelled next menu and a confidence value corresponding to the modelled next menu;
scoring each menu item from the set of menu items, wherein scoring each menu item is based at least in part on a user inclination parameter, a frequency ratio, and the confidence value associated with each menu item;
ranking each menu item based on each menu item scored;
program instructions to prune, from the set of menu items of the application, a menu item having a score based rank below a threshold rank, the prun

Assignees

• IBM

Inventors

• Fan Long
• Yang Yang
• Fan Ye
• Wu Juan
• Mao Qi
• Xu Jing James

Classifications

• G06F3/0482 Primary

  Interaction with lists of selectable items, e.g. menus · CPC title