# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 6 Machine Learning
### Deep Learning  
Recent studies used Deep Learning Networks (DLN) to construct recommendation models for automatic notifications, content classification, and pattern recognition [65; 68; 155]. DLNs differ from ANNs by the interconnection of multiple layers that handle various weights and trigger functions between the hidden layers' inputs and outputs. The deep architecture allows forward or backward propagation with adjustment of weights during feature learning and detection. Loss functions are used in classification or regression tasks to determine the difference between the labels predicted by the DLN and the actual labels in the dataset. Unlike ML, DLN models use unstructured data, reduce computational costs, and the performance scale is directly proportional to the data amount [237].Considering the cold start problem and the scarcity of CF algorithms' information, [76] developed a recommender based on a DLN and an MF of latent features to manage software projects.  
Convolutional Neural Networks (CNN) are DLN used to identify patterns in input data segments that operate in one, two, or three dimensions. Unlike classical ML approaches, CNN uses filters to automatically extract features, reduce complexity, and overfit with pooling layers [112; 186]. The specific class classification process is supported in Fully Connected Layers (FCL). Particularly in [226] used CNN to extract features from georeferenced images used to recognize human activities. Also, [36] proposed combining CNN and FCL models to extract affective features from physiological signals (ECG and GSR), surpassing traditional techniques' precision. These models (CNN and FCL) extracted affective features from multimedia text [156] and discriminatory features from optical flow images [165]. A hybrid CNN model [159] used one-hot vectors in the prediction of sentiment polarity.  
The Long Short-Term Memory (LSTM) approach is a version of the Recurrent Neural Network (RNN) that overcomes gradients' problems by remembering long-term sequential data, with its structure that includes inputs, outputs, and gates of forgetfulness regulated with the sigmoid function [237]. Specifically, in [164] integrated the 3 DCNN and LSTM algorithms to extract Spatio-temporal features in gesture detection. Some affective semantic analysis studies used the CNN and LSTM RNN algorithms in the classification of emotions from movie comments [9] and the detection of stress in psychological phrases [82]. Additionally, [48] proposed the CNN and LSTM algorithms to extract the contextual features of tourist attractions' sentences. In [158], the CNN and RNN techniques were used to predict the phrases related to the users' perception and intention to recommend smart wearable devices.  
Lately, the integration of ML algorithms and chatbots has enormous potential for recommending tourist destinations. In [238] designed a POI recommendation architecture based on decision trees to establish the profile of the user of a social network with the history of visits. Besides, in [239] proposed an LSTM RNN model to detect the users' interests based on the history of preferences. The query chatbot provided travel options based on the detected profile.  
Most studies showed better performance in emotion-sensitive SR when using DLN algorithms. In [150] customized an emotion-sensitive using a DCNN to classify songs in a dataset based on user profile and history of preferences. It defined latent features and musical relationships with the Weighted Feature Extraction (WFE) algorithm based on a weighted MF.