## 4 Related Work
### Affective Recommender Systems  
In general, recommender systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular user [52]. There are many references in published literature to recommender systems being used in human-computer interaction (HCI) using information about emotions to leverage multimedia content (e.g., music, video clips, text, and images) and help users to discover interesting new items based on their personal preference features [53]. Depending on the application in question, the features extracted from media items may be semantically oriented into prediction of particular target classes in the expectation that this will eventually have a positive impact on the final quality of recommendation. For instance, features for an automatic playlist generation system extracted from music items may be oriented into prediction of some predefined emotion or genre classes, human-generated tags (auto-tagger), or some latent factor features of collaborate models. Affective recommender systems (ARSs) are a type of RSs that make recommendations based on estimation of emotion using multimodal data fusion of various physiological signals, facial expressions, body posture, gestures, eye movements, voice, and speech [54]. The emergence of sensors and wearable devices as mechanisms for capturing physiological data from people in their daily lives has enabled research on detecting emotional patterns to improve user experience in different contexts. For example, in [55], the authors apply the deep learning approach to a dataset of physiological signals--electrocardiogram (ECG) and galvanic skin response (GSR)--to detect users' emotional states. In this study, relevant features are extracted from the physiological signals using a Deep Convolutional Neural Network (DCNN) to perform emotion recognition. The estimated valence and arousal values can be used to classify the affective state and as input to personal recommendation systems [55]. Further, it has been realized that emotions are important information to consider when suggesting instructional content to students in a learning environment, since it might change their emotional state [56]. To examine the current state of the influence of emotions in the field of education, particularly in content recommender systems, a systematic literature review of affective recommender systems in learning settings was conducted [56]. Finally, research should be noted concerning a video streaming service that uses the viewer's emotional reactions as the basis for recommending new content [57]. The researchers conducted a study where subjects' facial expressions and skin-estimated pulse were monitored while watching videos. Results of this study showed 70% accuracy in estimation of dominant emotions. However, no correlation was found between the number of emotional reactions participants have and how they rate the videos they watch. The pulse estimation was reliable to measure important changes in pulse [57]. The approach used in our work may be integrated into an affective recommender system. However, unlike the research on affective recommender systems mentioned above,our work focuses specifically on developing recommender systems for the construction of multimedia stimuli databases. Although several subtypes of RSs exist [52], such as collaborative filtering (CF), context-based, content-based, and session-based RSs, our approach would likely use only the content-based type of RS. Here, an image stimulus would be recommended for display to a user based on the similarity of the image in a constructed cluster (using the \(k\)-means algorithm) to an image previously shown to the user. The clusters are determined later in the paper and depend on the emotion pairs evoked by the image.