# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 3 Recommender Systems
### Evaluation of Recommender  
The evaluation datasets were extracted from social networks and publicly shared databases. The data has an overview of recommended items, user preferences, and historical reviews from visitors. Depending on the experimental design, the algorithms can implement cross-validation techniques. Initially, the item review dataset is split into a significant percentage to train the recommender and the other to test the model's performance. Some studies used the k-fold Cross-Validation (CV) technique [4; 11; 12; 50; 63] to verify the precision of each fold of the comparative methods. On the other hand, the Leave-One-Out Cross-Validation (LOOCV) technique [4] eliminates each user's item that ensures the impartiality of the system to recommend items that were left out of the training data. The challenge of providing high-quality recommendations involves using evaluation methods to extract value from the prediction from a technical and experimental POI [42; 58; 83]. In general, the recommendation and affective detection models according to the performance indicators used accuracy metrics (MAE and RMSE) [59], decision support metrics calculated in the confusion matrix (precision, recovery, and F1 score) [8; 13; 59; 72; 136; 158], and metrics with recognition of range (MRR and NDCG) [7]. * Accuracy: Measures the ratio of suggestions for relevant items compared to actual user ratings. Besides, it indicates the proximity of the results concerning the right recommendations. The precision metric was implemented in the works of [6; 6; 14; 50; 62; 7; 70; 82; 159; 163; 164]. * Mean Absolute Error (MAE) and Root Mean Square Error (RMSE): Compare the predicted scores' closeness to the actual ones and estimate the mean model's prediction error. In particular, RMSE assesses all rating inaccuracies, while MAE measures the average magnitude of prediction errors. Some RS investigations implemented these metrics [4; 6; 12; 53; 16; 155; 159; 165; 166]. * Precision: Calculates the percentage of selected items that are relevant to the user's recommendation [7; 13; 112; 116]. Also, the Metric Media Precision (MAP) metric compares the generated recommendation list with the list of relevant recommendations for users [17; 109]. * Area Under the Curve (AUC): Shows the relation between True Positive rates and False Positive rates. This metric is used as the recommender performance measure with a value close to 1 [13; 16; 63]. * Mean Reciprocal Rank (MRR): Identifies the first relevant item's location in the recommendation list. The elements relevant to the user must be located in the notable positions of the generated list [7; 109]. * Normalized Discounted Cumulative Gain (NDCG): Use a gain factor to consider the position in which each suggestion was relevant [7; 70]. * Hit rate: Represents the fraction of hits of the items in the recommendation list. Besides, it contains the preferred items associated with the current context of the user [8; 13; 116; 136]. Table 3 compares some recommender implementations that involved emotional data in the personalization of music, movies, tourist attractions, and online products. Initially, the recommended approaches were described previously (Content-based filtering CB, Knowledge-based KB, and Collaborative-Filtering CF). The data collection section lists the user model features and the datasets that provided the recommendation process's contextual factors. Then, the algorithms of the context-aware recommender system approaches were specified (pre-filtering PRE, post-filtering POST, contextual modeling CM, based on emotion EM, and SA sentiment analysis). Finally, in the machine learning section, the algorithms, similarity metrics (Sim), validation (Valid), and evaluation of the proposed recommendation models' performance results were synthesized.