As e-commerce websites began to develop, users found it difficult to find the most appropriate choice from the immense variety of items.Recommender System (RS) is a subclass of information filtering used to predict a rating that a user would give to an item. Recommender systems have been applied to several domains such as online streaming, Marketing, and e-commerce to assist in decision making.Recently, contextual information has been recognized as a useful factor in improving the quality of recommendations in different fields. However, it is under investigation in the area of online shopping. Among all contextual information, time is considered as one of the most important dimensions. This paper integrates time dynamics with implicit feedback (add to cart and Transactions) in an online shopping recommender system using the Matrix Factorization algorithm (MF). The integration is done using two approaches: The first approach is Bias in which the time is used as the third column in the user rating matrix. The second approach is the Decay function which produces new ratings by aggregating implicit feedback with time dynamics and gives a higher weight to the new items over older ones. Using the ?Retailrocket? online shopping dataset, the experimental results demonstrate the effectiveness of the decay function over the traditional context-aware matrix factorization CAMF (bias) in terms of precision, recall, and Mean Average Precision (MAP).