I have a system which sends invitations to users to participate in online questionnaires and want to use machine learning in order to predict the likelihood of fulfilling the questionnaires in a predefined time ( i.e. within 1 day , 2 day , 3 days ,a week, 2 weeks, etc) based on various feature related to the users to whom the invitations are sent to , details of the questionnaires ( i.e. how long are they , their topic ,etc.) , other contextual data ( time of day , day of year , in which media the invitation are sent - i.e. sms / email etc). I can train with positive examples ( invitations that were responded to by users ) and negative examples ( invitations that WERE not responded to ) , however , I'm not sure how to take into consideration the "predefined time" into the feature vector. for example, should I simply include a feature of "days since invitation was sent" and in the positive examples include the time , and in negative examples replicate each example X each of the predefined times to indicate that the users didn't respond at all ?
Any advise would be welcome !