Abstract: Unlike traditional recommendation systems that rely only on the user’s preferences, context-aware recommendation systems (CARS) consider the user’s contextual information such as (time, weather, and geographical location). These data are used to create more intelligent and effective recommendation systems. Time is one of the most important and influential factors that affect users’ preferences and purchasing behavior. Thus, in this paper, time-aware recommendation systems are investigated using two common methods (Bias and Decay) to incorporate the time parameter with three different recommendation algorithms known as Matrix Factorization, K-Nearest Neighbor (KNN), and Sparse Linear Method (SLIM). The performance study is based on an e-commerce database that includes basic user purchasing actions such as add to cart and buy. Results are compared in terms of precision, recall, and Mean Average Precision (MAP) parameters. Results show that Decay-MF and Decay-SLIM outperform the Bias CAMF and CA-SLIM. On the other hand, Decay-KNN reduced the accuracy of the RS compared to the context-unaware KNN.