Method and system of providing personalized guideline information for a user in a predetermined domain

2:

• The method according to claim 1, wherein the personality-typed machine learning models of the model pool were trained based on previously collected data.

3:

• The method according to claim 1, wherein a controller module is provided, wherein said controller module is configured to inform the individual personalized machine learning model.

4:

• The method according to claim 3, wherein every time the individual personalized machine learning model presents a recommendation prediction to said user, the individual personalized machine learning model is informed by the controller module, wherein the controller module in turn considers the recognized personality type of said user.

5:

• The method according to claim 3, wherein said controller module is configured to interact with said user.

6:

• The method according to claim 3, wherein the control module is configured to collect feedback information from said user when a recommendation prediction is presented to said user.

7:

• The method according to claim 3, wherein the controller module includes an emotional state recognizer, wherein the emotional state recognizer is employed to handle implicit feedback information of said user such that an emotional state of said user is determined.

8:

• The method according to claim 7, wherein the emotional state of said user is considered by the individual personalized learning model of said user, and/or
• wherein the emotional state of said user is used to reweigh the feedback information given by said user.

9:

• The method according to claim 6, wherein the feedback information is logged together with the recommendation prediction in a feedback database, wherein said feedback database is shared across all users of the predetermined domain.

10:

• The method according to claim 9, wherein, feedback information of users collected by the feedback database is used to update personality-typed machine learning models of the model pool and/or to update users' individual personalized machine learning models.

11:

• The method according to claim 9, wherein, if a final outcome of a user's logged feedback information is known, all feedback information data points of the user are readjusted by the final outcome in a post episode feedback adjustment.

12:

• The method according to claim 9, wherein a user similarity matrix is computed, wherein the user similarity matrix indicates how similar each user is to all other users, such that, based on a similarity score, logged feedback information of the feedback database is reweighed to create a personalized log for a specific user, wherein the personalized log is used to update the individual personalized machine learning model of the specific user.

13:

• The method according to claim 9, wherein, depending on a personality type mismatch between the personality type assigned to a user and a specific personality type of a specific personality-typed machine learning model that is to be updated, logged feedback information of the feedback database is reweighed to create a personality-typed log, wherein the personality-typed log is used to update the specific personality-typed machine learning model having the specific personality type.

14:

• The method according to claim 1, wherein the predetermined domain includes a gym environment, a smart home environment, a virtual reality environment, a shopping environment and/or a health environment.

16:

• The method according to claim 1, wherein the predetermined domain is in the context of everyday activities.

17:

• The method according to claim 6, wherein the feedback information comprises implicit and/or explicit feedback information.

18:

• The method according to claim 10, wherein the feedback information of users is used periodically.