## 5 Recommendation and Future Research Direction  
User satisfaction is the ultimate goal of any DRS which is developed primarily to take into account the dynamic user interactions. Even though a lot of attention has been given towards DRS in recent times, several challenges and possibilities exist that mark the future of research on DRSs. It is observed in this review that the challenge of dynamisms faced by RS occurs in various forms which comprises of user preference drift, items popularity drift, change of product perceptions and popularities, change in fundamental properties of items, dynamic interest within the community, seasonal effects, user-item bias shifting, changes in rating scales, long-term and transient or short-term changes. These issues have been dealt with by several studies at the individual level such as [22; 24; 63] where only the single concepts are tracked. A generic approach that would consider the integration of these factors has been rarely attempted [12]. The state-of-the-art temporal models in DRS are not flexible enough to be implemented for all challenges of dynamisms faced in the field of RS. It is desirable that a method is flexible enough to accommodate both the long-/and short-term preferences of users. Most of the DRS temporal models are limited to use only the preference at a particular time frame to model user behaviors for future recommendations. Some researches ignore the preference of past interactions, which in most cases may still have more impact at the time of making recommendations. Therefore, omitting such a piece of valid information may result in poor performance of DRS. Additionally, the current research is mainly focused on adopting temporal models on the assumption that change has already taken place in the user interaction data without applying any change detection models. There is a need to consider the change point detection methods in which detected changes could only be judged by employing such methods and carrying out a rigorous test; after which a necessary action can then be taken on either to update or discard the model whenever the change is certain. Another open challenge in modeling concept drifts in RS is the fact that many concept drifts tend to occur in different directions and at a different point in time, which requires different approaches to handle them. For example, [149] pointed out that many of the changes related to user preferences are driven by local factors that may not be properly captured by those methods that seek a global concept drift. Other contributions attempted to track global drifts on the item level through the data as a whole [68]. Another important issue in the study of concept drift in RS is measuring the patterns or rate of drifts. The learning algorithms should keep track of multiple changes and adapt to a different rate of change patterns. Most of the temporal models were build on major distinctions between the concepts that span for an extended period of time-gradual drifts, and those that change within a short period of time-sudden drift [143]. Therefore, there is a huge need for extensive research to explore more on these aspects to identify the suitable models that will ease the tracking and recommendation task and as well set a focal point of discussion on how to enhance the existing frameworks. Another good line of consideration that has not been well explored is the temporal deep learning approach to track temporal dynamics in RS. Deep learning is a class of machine learning techniques, that is used for representation learning [150; 151] using multiple layers of information-processing stages in hierarchical architectures. Due to the recent remarkable achievements of deep learning methods, several methods have been proposed to investigate the problem of concept drift using deep learning methods. For example, [107; 152] proposed an RNN model for dealing with the problem of the concept drift for time series anomaly detection to address challenges posed by sudden or regular changes in normal behavior.