Patent ID: 11946753
Assignee: ADOBE INC.
Field: Measurement (Instruments)
Classification: CPC G  H | IPC G  H

Claim 16:
17. A system comprising:
at least one memory device comprising a recommendation machine learning model that includes a reward function having a plurality of parameters that were learned during multiple training iterations to indicate expected values of recommendations; and
at least one processor configured to cause the system to:
determine frequencies of use associated with a plurality of historical event sequences previously used by a plurality of users of a plurality of client devices;
generate, for a user of a client device, a popular event sequence based on the frequencies of use associated with the plurality of historical event sequences;
generate a recommended event sequence by using the recommendation machine learning model to select an event sequence for recommendation based on the reward function, the recommended event sequence corresponding to a general recommendation provided to client device users as a default;
receive, from the client device, one or more user preferences with respect to one or more events by receiving at least one user interaction with one or more interactive elements corresponding to the one or more events via a graphical user interface of the client device;
generate a modified recommended event sequence using the recommendation machine learning model by modifying the reward function to include a weighting factor that modifies the plurality of parameters of the reward function via one or more preference weights that represent the one or more user preferences to modify how the recommendation machine learning model selects the event sequence for recommendation without retraining the recommendation machine learning model; and
provide, for simultaneous display within the graphical user interface on the client device, the recommended event sequence, the modified recommended event sequence, and the popular event sequence.