#### Topic: Classification and probability theory

Long not to tell I will try to invent the task and to adapt for it. Assume I predict the list from 5 books which will be bought by the user with the greatest probability. In total books of 24 possible. I have a model which on features of the user gives the forecast, it not bad works, but does not consider change of popularity of books in due course. And it is considerable. Assume I was trained on 06 month 2015 and I should make the forecast for 06 months 2016. For some user the forecast is received: [0.00273501 0.00273501 0.21609817 0.00411186 0.00925567 0.1917159 0.04742743 0.00293886 0.00331788 0.01592568 0.00443959 0.00344877 0.04274225 0.01281825 0.00274041 0.01408142 0.00274985 0.12125014 0.01134686 0.00730866 0.00296034 0.10991038 0.04232292 0.12561873] Number of a position in an array it also is the book. Further it is sorted and undertakes 5 with the greatest probabilities. But not an essence. In 15 year there was a following amount of books [1 0 3878 5 2347 40 531 226 131 0 0 46 2709 61 3 22 7 279 4248 183 7 5488 5513 10163] In 16 year this amount changed [0 0 6731 9 1872 8 211 216 152 290 33 956 1205 245 4 19 8 2933 4684 152 3 5082 8147 9031] And I would like to make correction of the forecast for value of change of popularity. For example, the third book almost twice grew on popularity. It would Seem it is necessary to divide an amount in 16 year for 15 year and to increase by probabilities of the forecast. But it is impossible so to do. Since for 6th book coefficient turns out unfairly big (40/8). I.e. it is necessary also total number somehow to consider. A question as it do?