## 4 Recommendations and Future Research Directions  
Even though a lot of attention has been attracted towards CARS, several challenges exist that need more attention by the current researchers. The state-of-the-art frameworks in CARS are not generic and flexible enough to be implemented across different domains of applications. It is imperative that a framework is flexible enough to accommodate as many domains of applications as possible. This will tremendously ease the recommendation task and set a focal point of discussion on how to improve the existing framework, rather than propose several different frameworks that are hard to integrate. Moreover, the incorporated contexts are not enough for tailoring personalized services to users. It is observed that only one or two contexts are incorporated by the researchers in the RS process such as in [62], and considering the dynamicity of users, incorporating a single context may not necessarily represent a user's interests and preferences. Additionally, most CARS systems are limited to current context in learning the users' profiles. Some researches ignore the context of past interactions, which in many instances are the pre-requisite to the current context, and neglecting such information may result in a poor performance of CARS. Furthermore, it is observed in this review, that most of the previous research does not handle heterogeneity of data and group recommendations despite their high significance, due to the complexity of users. Researchers only consider one set of context type such as \"Time of the Year\" and \"Intent of Purchase\", which in the case of e-commerce are all temporal contexts. \"URL\", \"ISBN\",  
Figure 8: Developmental stages in Context-Aware Recommender Systems (CARS). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Evaluation Metric** & **Condition** & **Merits** & **References** \\ \hline \multirow{3}{*}{Click Through Rate (CTR)} & To measure the number of recommendations end up being clicked & It provides a broad view of how well an item or a campaign attracts users. & [59; 68; 131] \\ \hline \multirow{3}{*}{Purchase Rate (PR)} & To measure the number of recommendations end up being purchased & It measures the number of times an item being purchased. & [59] \\ \cline{1-1} \cline{2-4}  & & & [76; 95; 112; 121; 123; 126; 127; 141; 142; 145; 173] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._\"Title\" in the case of e-document, which are all static contexts. \"Distance to the Point of Interest\" and \"Location\" in the case of Places are all spatial contexts. Another concern is the evaluation strategy used to evaluate CARS systems since almost all the reviewed research evaluated their systems either using offline evaluation or by user studies. Although such approaches are effective and necessary, the results mostly contradict when applied in real life applications using online evaluation [177]. Another important aspect that is almost always neglected in most of the previous studies is the issue of privacy and security. Current research into CARS does not enable privacy and security in the recommendation process, which is an important aspect of real-life recommenders. As CARS systems are expected to know a user better than one knows oneself, because of the vast accumulated knowledge coupled with the ability to predict what a user may need in the near future, systems with these abilities need to guarantee security, privacy, and safety. Furthermore, some CARS systems are vulnerable to shilling attacks [178], especially in the domain of e-commerce to generate positive ratings for their products and negative ratings for competitors' products. Researchers should be able to detect these attacks in order to instill confidence and trust in the use of CARS. Although the concept of incorporating contexts into RS process is new, the current research is mainly concentrated on a representational view of context. Taking an interactional view into cognizance is also important considering the dynamicity of users.