# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 3 Recommender Systems
### Content-Based Filtering  
A typical recommendation approach shares a mechanism to describe the detailed features of items that may be of particular interest to a user [42]. Based on the representation of these items, a user preference profile is built. Through an ML algorithm, it compares the item features with the user's profile and generates the recommendation list. The items' similarity is calculated based on the attributes associated with the compared items. For example, in a music recommender, a user rates a relaxation song with a high estimate, then the system learns to suggest other songs of the same emotional state. The song features can describe both structured data (song title, singer name, music genre, year of release, and emotional state) and unstructured data (user comments and song description).  
Some studies have used the Cosine Similarity (CS) metric [6; 43; 44] to determine the similarity of the items represented in the n-dimensional space vectors (for example, a matrix of similarity between songs and emotional state). In contrast, the Euclidean Distance (ED) [45; 46; 47; 48] was used to measure the actual distance between the elements and the user's profile. The recommenders' implementation based on content emerges as an alternative to personalize the multimedia, tourist, and entertainment content available on the Web. Emotions have aroused intense interest in the design of user preference models. For example, the influence of affective metadata on image rating performance using the Support Vector Machine (SVM) algorithm [49]. The travel profiles' definition using a multiple regression model implicitly obtained the users' preferences through the POI images [45].  
Due to the semantic ambiguity of unstructured data, Probabilistic Latent Semantic Analysis (PLSA) techniques have been proposed for POI image annotation and ontological representation of user profile data [50; 51; 52]. Also, [53; 54] described a tourism approach based on social relationships and user preference profiles to calculate the similarity of POIs. A hybrid approach in [7] compared the Rocchio algorithm for customizing required queries in the classification of candidate POIs with the k-Nearest Neighbors (kNN) weighted classifier query builder.  
Figure 4: Trends of approaches, frameworks, and applications in recommendation systems research.  
Although content-based approaches have limitations for predicting novel items, they have datasets that enrich domain knowledge and avoid cold start problems [55]. To overcome the problems of prediction accuracy, some researchers have proposed hybrid approaches. In particular, [56] presented a framework of tourist' mobile services based on the semantic relationship of the agreement of words and frequency of terms to determine the item's similarity to recommend. The architecture of a content-based and semantic-conscious SR [57] described the components from a computational perspective. It introduced a cleaning user-profile method and overcame the magic barrier problem by detecting the semantic similarity between the item and the profile. Besides, it used a filtering component to generate the recommendation list appropriate to the user's preferences.