Personalized recommendation systems to remediate inefficiencies in user behavior

A method for generating personalized recommendations predicted to remediate detected inefficient user behaviors, the method comprising:

• receiving, by one or more processors and from at least one data source, input data representative of a user, wherein the at least one data source includes an extended reality (XR) data source and the input data includes XR input data and non-XR input data;
• transforming, by the one or more processors, at least part of the input data representative of the user into transformed input data, the at least part of the input data including the XR input data and the transformed input data formatted for machine learning analysis;
• generating, by the one or more processors and using a first machine learning model, a user behavior pattern for the user based on the transformed input data representative of the user;
• classifying, by the one or more processors, the user behavior pattern based on one or more personas representative of user characteristics;
• identifying, by the one or more processors and based on at least the transformed input data and the one or more personas, an inefficiency in the user behavior pattern impacting a goal of the user, wherein the identifying the inefficiency includes:
• predicting, using a third machine learning model, a potential outcome for one or more actions consistent with the user behavior pattern, and
• identifying the inefficiency based on the potential outcome including a negative effect on the goal of the user;
• generating, by the one or more processors using a second machine learning model including a collaborative filtering model, a personalized recommendation for the user to remediate the inefficiency;
• generating, by the one or more processors, a personalized user recommendation display for the user;
• and
• automatically displaying, by the one or more processors, the personalized recommendation to the user via a preferred location of the personalized user recommendation display based on one or more user format preferences of the user.