## 2 Theoretical Background
### Overview of DRS  
The exponential growth of user-generated data in the Internet and information age in recent age has provided an important opportunity to business organizations to effectively mine and analyze customer feedback with regards to their satisfaction on products in real-time to achieve business objectives [19]. However, the exploding volume and speed of data growth have introduced several challenges to the predictive power of the algorithms applied to such streaming data sources. One of the prevalent challenges in such an environment is concept drift [20]. Concept drift in the field of data mining and predictive analytics is defined as unexpected changes in the underlying data distribution over time [21]. The monitoring of concept drift is a major challenge that has received much attention from researchers in different fields, comprises of traffic management, activity recognition, intrusion detection, fraud detection, and RS among others [21]. In the field of RS, the term concept drift is often used in different ways, such as temporal dynamics, temporal effects or temporal factors. According to [5], RS is one of the most rapidly changing environments where user interests and the associated recommendations witness changes with respect to time for many reasons, either circumstantial or natural. In e-commerce RS, for example, users redefine their taste when new items appear in the stock, or as a result of changes in family size. In the like manner, products are constantly going in and out of popularity as time passes by. As such, the traditional RS techniques that only take into consideration the user's historical ratings and usually ignore the changes that took place over time leads to underperformance of the models and consequently produced inaccurate recommendations [12; 22; 23; 24; 25]. To overcome the above challenges, much research has been conducted on DRSs to improve recommendation accuracy. DRSs are at the leading edge, gaining enormous research interest in recent age. In its broadest definition, a DRS is a kind of system that is intended to capture the time drifting concepts either from the user's side or item's side, by modifying their recommendations to meet the users' target accordingly [4]. Although DRS employs the three most common filtering algorithms such as Content-Base (CB), Collaborative Filtering (CF) and Hybrid-based methods just like the traditional static recommendation approaches. However, they are designed to perform differently in a way that the challenges of dynamic data stream facing RS environments are addressed. In recent times, several of secondary studies have been conducted. One of the early study reported the benefits and challenges in DRS [4]. This study examined several studies in the literature that focused on the benefit of dynamic properties in boosting the performance of the static RS. Furthermore, they classified the selected studies according to the dynamic parameters involved, which are considered as the main factors for building DRS. The study identified the challenging factors such as finding better ways in which the user's temporal characteristics and expectations could be extracted through the combination of implicit and explicit feedback. [26] presented a comprehensive review on time utilization in the RSs as a key factor to enhance the quality of recommendations. [14] provided a systematic review which focuses mainly on the context-aware RS. The review study discussed the important steps for CARS developmental process. The authors further provide the overview of the state-of-the-art methods for CARS and classified the reviewed studies according to their application domain, filtering models, and evaluation methods in order to provide a better understanding of each process for the novice and new researchers. In line with the current efforts toward looking into these important aspects of the dynamism in RS, we attempt to cover various types of concept drifts from existing works in the literature.