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Nowadays, with the increase in the amount of information generated in thewebspace, many web service providers try to use recommender systems topersonalize their services and make accessing the content convenient.Recommender systems that only try to increase the accuracy (i.e., thesimilarity of items to users' interest) will face the long tail problem. Itmeans that popular items called short heads appear in the recommendation listsmore than others since they have many ratings. However, unpopular items calledlong-tail items are used less than popular ones as they reduce accuracy. Otherstudies that solve the long-tail problem consider users' interests constantwhile their preferences change over time. We suggest that users' dynamicpreferences should be taken into account to prevent the loss of accuracy whenwe use long-tail items in recommendation lists. This study shows that the tworeasons lie in the following: 1) Users rate for different proportions ofpopular and unpopular items over time. 2) Users of all ages have variousinterests in popular and unpopular items. As a result, recommendation lists canbe created over time with a different portion of long-tail and short-headitems. Besides, we predict the age of users based on item ratings to use morelong-tail items. The results show that by considering these two reasons, theaccuracy of recommendation lists reaches 91%. At the same time, the long tailproblem is better improved than other related research and provides betterdiversity in recommendation lists in the long run.

Considering user dynamic preferences for mitigating negative effects of long tail in recommender systems