## 3 Classification of the Reviewed Literature
### Application Domain and Their Incorporated Contexts
#### 3.2.3 Multimedia Domain  
One important consideration of this literature review in the multimedia domain is the utilization of social media to personalize multimedia packages. Users are now overwhelmed with the huge quantity of social media resources and services. Authors of [74] proposed an RS model that utilizes these resources, which also incorporate rating information and social tags to personalize content search given a particular context. Their experimental results revealed the feasibility of personalized recommendations according to users' contexts and improving the cold start situations. The potential of exploiting and utilizing social contents has been presented in [75]. The model uses a tensor to leverage the latent preferences associated with multiple dimensions of a user, item, and context for multimedia recommendations. The utilization of functionalities from various technologies in a heterogeneous environment such as sensors has also been presented in [76], to propose a multimedia recommender system that determines the best service and simultaneously accommodate the context of the devices in the heterogeneous environment.  
Another important consideration is the group recommendation and the utilization of Ambient Intelligence (AmI) environment for personalized recommendations. AmI is an electronic environment capable of sensing and responding the presence of people with the help of some set of promising technological approaches. An interactive and intelligent framework has been proposed in [77], which combines sensors with Ambient Intelligent to implicitly provide inputs to the RS systems to enhance users' satisfaction. The framework also has the ability to personalize recommendationsfor the group of users instead of individual users. Using offline experiment, the technique has shown significant improvement in given out personalized recommendations according to users' contexts. The authors of [78], extract and integrate contextual information out of music playlist names to propose a collaborative approach that clusters them to generate music recommendation.  
Additionally, tags are part of contextual information characterizing multimedia content, tagging becomes of utmost importance. However, the number of users that provide raw tags are so limited and therefore insufficient to characterize the whole information of web videos. In trying to provide more tags to multimedia contents, researchers proposed tag recommenders. Specifically, the authors of [79] proposed a context-oriented tag RS approach to recommend important tags to web videos. While [80] suggest a personalized tag recommendation system that model contextual information using a method called Pairwise Interaction Tensor Factorization (PITF). Experimental results of both approaches proved increased performance.  
Finally, as [81] suggests the view of context into representational and interactional. It is important to realize the fact that, different from all other authors of our reviewed literature, the authors of [82] proposed a software framework capable of handling the activity-oriented and dynamic nature of contexts. With the proposed framework, developers can develop CARS applications capable of generating recommendations for both group users and individuals.