  Nowadays, with the increase in the amount of information generated in the
webspace, many web service providers try to use recommender systems to
personalize their services and make accessing the content convenient.
Recommender systems that only try to increase the accuracy (i.e., the
similarity of items to users' interest) will face the long tail problem. It
means that popular items called short heads appear in the recommendation lists
more than others since they have many ratings. However, unpopular items called
long-tail items are used less than popular ones as they reduce accuracy. Other
studies that solve the long-tail problem consider users' interests constant
while their preferences change over time. We suggest that users' dynamic
preferences should be taken into account to prevent the loss of accuracy when
we use long-tail items in recommendation lists. This study shows that the two
reasons lie in the following: 1) Users rate for different proportions of
popular and unpopular items over time. 2) Users of all ages have various
interests in popular and unpopular items. As a result, recommendation lists can
be created over time with a different portion of long-tail and short-head
items. Besides, we predict the age of users based on item ratings to use more
long-tail items. The results show that by considering these two reasons, the
accuracy of recommendation lists reaches 91%. At the same time, the long tail
problem is better improved than other related research and provides better
diversity in recommendation lists in the long run.
