A method for determining an automation setting preference comprising: receiving contextual data for an automated system in a venue in which a plurality of users are currently located; accessing individual preference data for individuals in the plurality of users, wherein the individual preference data comprises a value for an automated setting of the automated system selected by an individual user along with contemporaneous contextual data; accessing group preference data for one or more sets of users in the plurality of users, wherein the group preference data comprises a record of settings for the automated setting when a set of users experienced the setting together; automatically determining a primary preference value for the automated setting using the contextual data, the individual preference data, and the group preference data as input to a machine-learning process; outputting a control signal to implement the primary preference value as the automated setting value; characterized in that the method further comprises receiving a user input to change the automated setting value in the automated system from the primary preference value to a secondary value that is different from the primary preference value; identifying a contextual feature value in the contextual data that relates a group member in the plurality of users; calculating a feedback impact score for the contextual feature, the feedback impact score measuring a change in score associated with one or more preference values for the automated setting that would result from being able to establish a relationship between the secondary value and the contextual feature value, wherein the change in score is for a measure of fit between the contextual data and the setting value calculated by the machine-learning process; determining that the feedback impact score exceeds a feedback threshold; generating a feedback solicitation upon determining that the feedback impact threshold exceeds a feedback threshold; outputting the solicitation; and receiving feedback and retraining the machine-learning process using the feedback as training data that links a secondary preference for the automation setting to the contextual factor, wherein the feedback solicitation is built using the contextual feature, a value for the contextual feature, and the secondary preference. receiving contextual data for an automated system in a venue in which a plurality of users are currently located; accessing individual preference data for individuals in the plurality of users, wherein the individual preference data comprises a value for an automated setting of the automated system selected by an individual user along with contemporaneous contextual data; accessing group preference data for one or more sets of users in the plurality of users, wherein the group preference data comprises a record of settings for the automated setting when a set of users experienced the setting together; automatically determining a primary preference value for the automated setting using the contextual data, the individual preference data, and the group preference data as input to a machine-learning process; outputting a control signal to implement the primary preference value as the automated setting value; characterized in that the method further comprises receiving a user input to change the automated setting value in the automated system from the primary preference value to a secondary value that is different from the primary preference value; identifying a contextual feature value in the contextual data that relates a group member in the plurality of users; calculating a feedback impact score for the contextual feature, the feedback impact score measuring a change in score associated with one or more preference values for the automated setting that would result from being able to establish a relationship between the secondary value and the contextual feature value, wherein the change in score is for a measure of fit between the contextual data and the setting value calculated by the machine-learning process; determining that the feedback impact score exceeds a feedback threshold; generating a feedback solicitation upon determining that the feedback impact threshold exceeds a feedback threshold; outputting the solicitation; and receiving feedback and retraining the machine-learning process using the feedback as training data that links a secondary preference for the automation setting to the contextual factor, wherein the feedback solicitation is built using the contextual feature, a value for the contextual feature, and the secondary preference.