User based collaborative filtering algorithms are widely used for generating recommendations for users. Standard user based collaborative filtering algorithms do not consider time as a factor while measuring the user similarities and building the recommendation list. However, users' interests often shift with time. Recommender systems should therefore rely on recent purchases of the users to address this user dynamics. Items also have their own dynamics. Most of the items in a recommender system are widely popular just after their releases but do not sell that well afterwards. Giving more importance to the recent purchases of the experts may capture the item dynamics and hence result in better recommendation accuracy. We study the performances of different time-aware user based collaborative filtering algorithms on several benchmark datasets. The proposed algorithms use the time-of-purchase information for calculating user similarities. The time information is also used while combining the purchase behaviors of the experts and generating the final recommendation. Copyright © 2012 SciTePress - Science and Technology Publications.