# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 3 Recommender Systems
### Collaborative Filtering  
Unlike content-based filtering, Collaborative Filtering (CF) automatically learns the relationship of items, extracts their features, and discovers new interest items to users [55]. FC methods generate user-specific item recommendations based on rating patterns from multiple users who share similar preferences [42]. The data sources indicate the behaviors and interests that users have had in the past concerning the products. These can be implicit (for example, tourist attraction reviews, review history, and search patterns) and explicit (for example, scaling from 1 to 5 to quantify liking for a tourist site). The ratings recorded by users are related to the dataset elements and form a two-dimensional matrix. CF recommendation models calculate similarity weights between users and items [58]. User-Based Collaborative Filtering (UBCF), also known as neighborhood-based, establishes a target user's neighborhood by analyzing historical behavior and preferences to find the best similarity between other users' items similar to the liked target user [8; 15; 16; 59]. In comparison, Element Based Collaborative Filtering (IBCF) predicts the rating of a new item and weights the ratings of the item set by the similarity of the target user behavior [8; 17]. The CF approaches used Pearson's Correlation Coefficient (PCC) [7; 14; 16; 59; 60; 61] and CS [4; 8; 13; 62] metrics to generate a list of product recommendations of interest to the target user. The recommenders, faced with the problem of cold start and the scarcity of user behavior data, have implemented mining and affective computing techniques to obtain implicit information [4; 14; 16; 63]. In personalization of tourist attractions and multimedia content, CF hybrid models merged the emotions of user comments, contextual data, and explicit's ratings available on online social networks [7; 53; 64; 65]. Tourist destination recommenders used CF review extraction methods to refine user preferences and article reputation [4; 54]. In contrast to CF algorithms, model-based approaches are categorized into factoring machine, matrix factoring, and ML algorithms. These models are scalable and handle sparse data [42; 58]. The Factoring Machine (FM) is a general-purpose regression method that models the interaction between contextual variables [42]. The Stochastic Gradient Descent (SGD) algorithm with regularization hyperparameters optimized the recommenders' FMs that integrated tourist attractions' features and affective factors [63; 66; 67]. Too, Matrix Factorization (MF) is a model of latent factors represented in a three-dimensional grade cube denoted by users, items, and values of the contextual dimension [4; 42; 58; 68; 69]. Furthermore, the Singular Value Decomposition (SVD) algorithm transforms the original rating matrix \(R=\mathit{users}*\mathit{items}\) into a matrix of users with latent features \(U~{}=~{}\mathit{users}*\mathit{latent}\mathit{factors}\). Then, it calculates the transpose of the original rating matrix \(RT~{}=~{}\mathit{items}*\mathit{users}\) and generates a matrix of items with latent features \(RT~{}=~{}\mathit{items}*\mathit{latent}\mathit{factors}\). Lastly, the prediction function for a specific user rating is given by \(R=U*\mathit{MT}\)[14; 58; 70]. Simultaneously,The SVD ++ algorithm is a specific variant of SVD that handles both implicit and explicit interactions [42; 58]. Some studies [71] modified the SVD ++ model by merging user sentiment and tourist destinations' temporal influence in the POI recommendation. Also, [72] used the emotion label's weighting as a tensor value of the High-Order Singular Value Decomposition (HOSVD) method to consider the preference and interest in movies' suggestion. Recently, the research challenge of developing recommendation models with a contextualized approach arises to overcome users' limitations in terms of geographic coverage and social interaction [55]. Most recommender architectures are hybrid because they combine various approaches with IBCF and UBCF [73].