# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 3 Recommender Systems
### Context-Aware  
In recent years, contextual information has been significant in describing current user behavior, scenarios, and mobile recommenders' application domain [89; 126; 127; 128]. Contextual information can involve various contexts related to user features, technological resources, and physical conditions [42; 58; 129]. The first involves user interaction on social media, mood, experiences, and preferences. The second describes the communication and computing capabilities of the user's ubiquitous devices. The last one specifies using the sensors to measure the climate, the weather, and the recommendation's location. For the above, some studies proposed a multi contextual perspective of mobile tourism SR by integrating users' location with environmental, temporal, and social factors to generate more effective predictions [93; 130; 131; 132; 133]. Unlike traditional recommendation approaches, the Context-Aware Recommender System (CARS) added contextual information to the multidimensional classification prediction function _user \(\ast\)item \(\ast\) context \(\longrightarrow\) rating_[42]. The three CARS categories that adapted the user's contextual information in a prediction model are pre-filtering, post-filtering, and contextual modeling [42; 58]. Pre-filtering, preference data is selected according to the context before algorithms calculate predictions [22]. In post-filtering, context is used to filter recommendations once predictions have been calculated with a traditional approach [22]. In contrast, contextual models incorporate contextual data directly into the prediction model. Some studies [15; 134] demonstrated better results in the suggestion of movies by incorporating contextual dimensions of the emotional [135] to the context-sensitive algorithms (items, users and User Interface, UI), Differential Relaxation Context (DCR), and Differential Context Weighting (DCW). Similarly, [16] used a hybrid CF approach based on mood, the fusion of preferences, and users' ratings with similar interests. While [70] adopted multiclass classification algorithms (Decision Tree - DT, Random Forest - RF and SVM) to predict interactive emotional states. Furthermore, musical CARS investigations [8; 17] have used CF approaches to extract emotional labels from songs associated with users' physiological states. Also, they implemented neural network models for the representation of the users' musical sequences [136]. Semantic Web techniques have enabled recommenders to add reasoning ability to context information. Ontologies semantically describe the concepts for modeling the features of user profiles, preferences, and items [80; 81]. The personalized recommenders of tourist activities are based on ontologies built from various data sources (travel motivations, user opinions, geographic information, ratings, among others) [137]. Particularly [61] proposed a travel SR based on the contextual information of emotions [138] extracted from social networks with semantic analysis techniques. Additionally, [139] proposed a cultural hybrid SR of personalized itineraries based on social networks' activities, the linked open data, and the physical context. For this, it implemented the semantic-based match algorithm for the user's profile. Also, POI recommenders have implemented mining techniques to identify contextual user preferences in social media reviews. In [116] generated the candidate POIs with the Adaptive KNN and Social Pertinent Trust Walker (SPTW) algorithms. Then, it displayed the recommendations with the Hybrid Selection Score (HSS) method. Another study [59] incorporated pre-filtering user preferences and a CF algorithm based on its proximity. On the other hand, [69] proposed the Largest Deviation technique to estimate the selective, parsimonious, and most relevant context of user preferences when rating POI items. Compared to traditional SR frameworks, the majority of CARS research demonstrated better performance on prediction results when implementing sentiment analysis and sentiment mining techniques [140].