## 3 Classification of the Reviewed Literature
### Extraction Approaches  
Extracting contexts is a crucial stage in the CARS developmental process; this is because the context is the major determining factor in any CAR system. The different extraction approaches adopted by researchers are explored in this section, in order to deduce the most advantageous approaches, which will help guide the choice of novices and new researchers. When the required information is identified an extracting method is applied to that information to construct user profiles, which is used to model the interests and preferences of users. This task is very difficult, as most users might not be sure of their interest [147], and even if they are, they're often reticent to make any efforts in its creation [148]. Depending on the nature of the context, contextual information can be extracted explicitly [149; 150], implicitly [151; 152], or using a machine learning approach [153]. In an explicit approach, users are required to provide the required information that describes their interests usually through ratings or asking direct questions. The system must be familiar with the vocabularies used. Information can also be acquired dynamically by building a user profile that captures the preferences of users automatically from the environment. An initial set of keywords is usually provided by the user to initiate the process [148]. The system then subsequently uses this information to identify the documents and services potentially fitting the user's interests, and appropriately presents them. Machine learning is also used to design an automatic recommender system [154]. The idea is to use a statistical or data mining approach to infer the user's contextual information by monitoring his/her activities with the system rather than asking him/her to provide a predefined set of keywords describing his/her preferences. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Domain of Application** & **Incorporated Contexts** & **References** \\ \hline Travel and Tourism & Time, companion, location, vicinity, current situation, social relations, & [89; 90; 91; 92; 93; 94; 95; 96; 97] \\  & intent, seasonality, nationality, budget, expertise. & [89; 90; 91; 92; 93; 94; 95; 96; 97] \\ \hline Places & Current time, location, companion, distance to an available point of interest, & [82; 83; 84; 89; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 111; 112; 113] \\  & relations, personal preferences, social influence. & \\ \hline Multimedia & Who, when, where, what, location, time, crowd, mood, companion, social, & [50; 74; 75; 76; 77; 78; 79; 80; 82; 84; 92; 108; 109; 110; 111; 112; 113] \\ \hline e-Documents & Activity, technology, location, environment, background, device, URL, & \\  & gender, time of the day, age, previous logs, ISBN, title, publisher, author, & [35; 61; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123] \\  & keyword, abstract, introduction, main idea, conclusion, paper & [124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160; 161; 162; 163; 164; 1 Figure 4 presents the distribution of our reviewed papers by their extraction approaches. It can clearly be seen from the figure, that researchers usually combine both the explicit and implicit approaches when extracting users' information. This is because combining the two methods help in utilizing the advantages and getting rid of the drawbacks of each approach. In addition, combining the two approaches increases the flexibility of the RS. On the other hand, researchers preferred using implicit data than the explicit, this is because even though the explicit data provide the most reliable source of information, explicit data extraction is difficult to perform as many users do not want to give out their information and besides they might not be sure of their interest [147].