Recommendation Taking into Account the Time Aspects of Users and Items

The role of reference systems is based on previous user activity that predicts interest in new item
s (e.g., Ecommerce Products). Most systems, however, do not take into account contextual aspects such as time, place, or contemporary society of other people. In particular, time aspects can play an important role for recommendations. In the case of a user, this may change his preferences. In the case of an item, it may be current (for example, when creating a weekly bulletin) or regularity (for example, when recommending seasonal items).
We analyze existing systems that take into account time-based systems and than design our own system for recommendations that take into account aspects of user time and recommended items to create more accurate recommendations. The proposed solution will be experimentally verified in the selected domain.