## 1 Introduction  
In recent times, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users' needs through mapping the available products to users based on their interests towards items [1]. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended [2]. This complex and dynamic data characteristic brought a big challenge in producing accurate recommendations as a result of these shifts in the relationship between users and items to be recommended [3]. In response to these dynamic relationships between users and items to be recommended, dynamic recommender systems (DRSs) were built to improve recommendation accuracy [4]. DRSs are kinds of systems that were intended to capture the temporal changes within different spheres, such as user or items related data as well as other time changing phenomena, by modifying their recommendations to meet the users' needs accordingly [2]. The recent progress in DRSs is motivated following the success of [5] in the Netflix movie competition held in 2009. The team considered three sources of time drifting concepts as key factors, such as user biases, item biases and changes in user preferences while combining multiple baseline models. It was proven that the incorporation of these factors makes Netflix's algorithm to perform best for rating predictions. Subsequently, many of the research works have been carried out to consider more types of concept drifts in RS [2; 5; 6; 7; 8]. For example, [9] proposed a framework for temporal RS over tweet stream which takes into account sudden shifts of users' interests and popularity of topics as time passes by. This was aimed to address the challenges of existing RSs and to provide the right topics for users at the right time. [10] provides a dynamic model that integrates the user interests and their evolving preferences on points of interest in a point-of-interests (POI) RS in a specific period of time. Other studies approached the concept drift of user interest by adopting the clustering and time-aware factor model to monitor the degree of user interest drift over time [11; 12] proposed a temporal model that was able to capture multiple drifts in RS such as the change of user's interests, fundamental item properties and the changed properties of items using a deep learning approach. The finding from all these approaches have shown that tracking concept drifts in RS leads to significant achievements and yield better results over the traditional RS techniques. Sequel to these achievements, a number of research work in this field has become exponentially increased. As a result, several efforts have been made to conduct different surveys and overviews of temporal models as well as dynamic parameters to serve as a guide for both the novice and new researchers to better understand the fundamental issues of DRSs and the challenges of these techniques [4; 13; 14; 15; 16; 17]. Specifically, the authors of [4] provided a review of the issues related to DRSs and discussed various time drifting parameters for the classification of DRS. The study has identified six main categories of these parameters such as temporal changes, real-time dynamics, context, diversity, novelty, and serendipity to help researchers in understanding the important lessons on different categories of DRS. In addition, different studies were also conducted to rigorously review the context parameter, in particular, to analyze the overall processes as well as design considerations in context-aware RS (CARS) [14; 15; 16; 17; 18]. For example, [14] conducted an SLR on the architectural principles to develop CARS. The authors identified the contextual information that is considered relevant for the development of CARS and categorized them into spatial, temporal and static groups based on their in-depth analysis. The most recent work in this category is [18].