Scalable Mixed-Effect Modeling and Control

A computer-implemented method for modeling mixed effects in a networked environment, the method comprising:

• obtaining, by a computing system comprising one or more processors, session data descriptive of one or more user sessions in the networked environment;
• initializing, by the computing system, a mixed effects model configured to describe a first effect and a second effect on a distribution of the session data;
• optimizing, by the computing system, a weighted objective over a plurality of subsets of the session data, the weighted objective comprising a weighting parameter configured to adjust, respectively for the plurality of subsets of the session data, a contribution of the second effect with respect to the first effect;
• and
• updating, by the computing system, the mixed effects model based on the optimized weighted objective.

A computing system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing instructions that are executable to cause the computing system to perform operations, the operations comprising:
• obtaining session data descriptive of one or more user sessions in a networked environment;
• initializing a mixed effects model configured to describe a first effect and a second effect on a distribution of the session data;
• optimizing a weighted objective over a plurality of subsets of the session data, the weighted objective comprising a weighting parameter configured to adjust, respectively for the plurality of subsets of the session data, a contribution of the second effect with respect to the first effect;
• and
• updating the mixed effects model based on the optimized weighted objective.

One or more non-transitory computer-readable media storing instructions that are executable to cause a computing system to perform operations, the operations comprising:

• obtaining session data descriptive of one or more user sessions in a networked environment;
• initializing a mixed effects model configured to describe a first effect and a second effect on a distribution of the session data;
• optimizing a weighted objective over a plurality of subsets of the session data, the weighted objective comprising a weighting parameter configured to adjust, respectively for the plurality of subsets of the session data, a contribution of the second effect with respect to the first effect;
• and
• updating the mixed effects model based on the optimized weighted objective.