## 3 Classification of the Reviewed Literature
### Information Filtering Approaches  
Determining when and how to incorporate contexts remains a challenge in the field of CARS [168]. This section explores the current trends to incorporate contexts into the recommendation process. When all the required information is in place, the next stage is to decide on which of the filtering approaches is to be applied. As indicated earlier, most papers in the field of CARS are focused on the representational view of context [81], whereby they assume it to be based on observable factors that have an effect in the recommendation process, which may not change significantly over a period. The authors of [169], go further to categorize this representational view into pre-filtering, post-filtering, and contextual modeling. This paper classifies the reviewed literature based on this categorization with a description of each in Table 4.  
Figure 5: Classification by modeling approach. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Extraction Method** & **Modeling Approach** & **Methods** & **References** \\ \hline \multirow{4}{*}{Implicit} & \multirow{4}{*}{Ontology} & Information is gathered implicitly and employ is used to model and build this information to provide personalized services. & [59; 79; 123; 139] \\ \cline{2-5}  & & & Information is gathered implicitly, which helps to reshape the relationship between users and contexts. & [69; 74; 106; 107; 112; 113; 157; 158; 159; 160; 161; 162] \\ \hline \multirow{4}{*}{Craph} & \multirow{4}{*}{Graph} & A random walk is usually performed to the graphs that were generated by the contextual information to infer the most suitable choices. & [90; 103; 113; 163] \\ \cline{2-5}  & & & Information is gathered explicitly and ontology is used to model and build this information to provide personalized services. & [109; 164] \\ \cline{2-5}  & & & Information is gathered explicitly, which helps to reshape the relationship between users and contexts. & [36; 137] \\ \hline \multirow{4}{*}{Machine learning} & \multirow{4}{*}{Vector} & Through data mining approach, they tracked users and infer static and dynamic contexts that were used to personalized recommendations. & [138] \\ \hline \multirow{4}{*}{Craph} & \multirow{4}{*}{Graph} & A random walk is usually performed to the graphs that were generated by the contextual information. & [79; 90; 121; 135] \\ \cline{2-5}  & & & Information is gathered both implicitly and explicitly, ontology is then used to model and build this information to provide personalized services. & [61; 62; 137; 165] \\ \cline{2-5}  & & & Information is gathered both implicitly and explicitly, which helps to reshape the relationship between users and contexts. & [75; 77; 80; 101; 105; 110; 120; 141; 144; 145; 146; 147; 148; 149; 150; 165; 166; 167] \\ \cline{2-5}  & & & Contextual information is represented in form of markup using attributes and contents that are used to infer their probability distribution for making recommendations. & [50] \\ \cline{2-5}  & & & Contects are represented as a set of conditions in which concluding expressions or facts may be derived from. & [89] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Modeling and extraction approaches. The advantage of both pre and post-filtering approaches is their ability to allow deployment of numerous 2D-RS techniques as the data-filtering query is constructed. The former uses the exact context and the latter adjusts the recommendation, while the contextual modeling approach incorporates contextual information with the classical 2D predictive models. Each of these filtering algorithms, are classified as [170; 171; 172]: (a) collaborative, (b) content-based, (c) demographic, (d) knowledge-based and (e) hybrid filters. As observed from Figure 6, most of the previous research adopts a pre-filtered approach to incorporate the contexts into the RS process.