# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 3 Recommender Systems
### Emotion-Based  
Affective computational models are increasingly efficient in generating personalized recommendations by detecting the user's emotions. Understanding and predicting user behavior is vital to an affect-sensitive recommendation system. Emotions are closely related to people's physical features and are considered a relevant contextual factor in the recommendations [46; 49; 145; 146]. Some studies [147; 148] designed user models based on personality traits and emotional states. These models comprise a conceptual level composed of profile data, physiological measures, contextual data, and subjective user attributes. In contrast, the specific domain level defines the connection between emotional states and affective elicitation attributes that can influence the recommendation process.  
The emotional information of users can be obtained with explicit and implicit methods and in a non-intrusive way. [PERSON] [63] integrated the prediction model of long-term users' moods, and the fashion recommendations improved compared to short-term emotions. [PERSON] [67] presented a recommendation system sensitive to affect that infers the emotional features [135] of multimedia contents. It used a cluster-based Latent Bias Model (LBM) to predict the probability that a user would click on images taking into account emotional contexts, mobile behavior, and social closeness.  
The exponential growth of content on online social networks has made it possible to identify users' affective features to improve recommendation quality. Emotional data is restricted by the scarcity and noise of user reviews. However, emotional information extraction avoids negative posts with the probability of increasing precision in the prediction [83]. In [13] developed a recommendation sensitive to the effect with a lexicon of emotions [135] extracted from the comments of the location social networks [102], and based on the emotional context, it generated a list of points of interest. In [14], a recommender based on implicit feedback data merged social information, rating, and emotion, maximizing the probability of user selection behavior through hybrid features.  
Some emotion-sensitive SR approaches use social information to prompt users to provide implicit feedback on an item's rating. In [73] presented an algorithm that extracts emotional information from a social network's digital element rating. Then, it used the user satisfaction scale to generate a list of neighbors based on the similarity of emotions. In addition to the products' rating, both the textual emotion analysis that detects affective polarization and the extraction of the labels (user preferences and intrinsic attributesof the product) favor user satisfaction when purchasing products [60; 62; 72]. In [149] defined emotional contagion and user satisfaction in a group recommender that suggests sequences of items obtained with emotion decay and mood assimilation that impact future items' satisfaction.  
Emotion-sensitive SR architectures improve the user experience by implementing services adapted to the current emotional state. In [150] developed a song recommender based on contextual data, current emotion, and musical preferences. It showed a better prediction when incorporating the emotions (happy, neutral, and sad) concerning the recommenders of similarity of content and feedback of the electroencephalogram signals. In particular, [151] designed a platform sensitive to emotions to improve people's productivity in smart offices. It proposed a module that recognizes the emotional context by obtaining data from sensors (temperature and humidity), detecting emotions (facial expression, voice and text analysis), and information from the Internet. Then, semantic rules were used in the task automation module.