Collaborative filtering (CF) is a recommendation technique that has been widely used in recommender systems. This recommendation technique provides recommendations by collecting the preferences of similar users in the recommender systems. CF predicts the active user’s preferences based on interests of users who share similar interests with the active user. This technique constructs a user’s profile based on his or her preference ratings. Then, a set of users who have similar ratings or similar interests with active user is determined as neighboring users. Once a neighborhood is formed for active users, CF makes a top-n item set which active user is most likely to purchase by analyzing the items in which neighbors have shown interest in. Unfortunately, CF may lead to the poor recommendation due to the lack of item ratings from users or explicit feedback in the real-world systems. CF became more realistic since it takes implicit feedback and data mining technique into consideration to provide better recommendations. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate the users’ preferences (can improve insufficient ratings) by providing more evidence and information through the observation made on users’ behaviors. Besides, Data mining technique which is the focus of this research can be effective in making recommendation to the user by analyzing user preferences.  In order to investigate the state of research and practice of CF and implicit feedback, a systematic literature review (SLR) of the existing published studies related to topic area in CF and implicit feedback are conducted. This research employs a case study in investigating users’ activities that influence a recommender system which is developed based on CF. Finally, Experimental evaluation method is opted to investigate and evaluate the enhanced CF technique.