Survey: Affective Recommender Systems Techniques

Raheem, Khamael Raqim; Ali, Israa Hadi

doi:10.1088/1757-899x/928/3/032042

Cited by 2 publications

(1 citation statement)

References 59 publications

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“…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.…”

Section: Affective Recommender Systemsmentioning

confidence: 99%

Investigation of Relationships between Discrete and Dimensional Emotion Models in Affective Picture Databases Using Unsupervised Machine Learning

Horvat

Jović

Burnik³

2022

Applied Sciences

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Digital documents created to evoke emotional responses are intentionally stored in special affective multimedia databases, along with metadata describing their semantics and emotional content. These databases are routinely used in multidisciplinary research on emotion, attention, and related phenomena. Affective dimensions and emotion norms are the most common emotion data models in the field of affective computing, but they are considered separable and not interchangeable. The goal of this study was to determine whether it is possible to statistically infer values of emotionally annotated pictures using the discrete emotion model when the values of the dimensional model are available and vice versa. A positive answer would greatly facilitate stimuli retrieval from affective multimedia databases and the integration of heterogeneous and differently structured affective data sources. In the experiment, we built a statistical model to describe dependencies between discrete and dimensional ratings using the affective picture databases NAPS and NAPS BE with standardized annotations for 1356 and 510 pictures, respectively. Our results show the following: (1) there is a statistically significant correlation between certain pairs of discrete and dimensional emotions in picture stimuli, and (2) robust transformation of picture ratings from the discrete emotion space to well-defined clusters in the dimensional space is possible for some discrete-dimensional emotion pairs. Based on our findings, we conclude that a feasible recommender system for affective dataset retrieval can be developed. The software tool developed for the experiment and the results are freely available for scientific and non-commercial purposes.

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Section: Affective Recommender Systemsmentioning

confidence: 99%

Investigation of Relationships between Discrete and Dimensional Emotion Models in Affective Picture Databases Using Unsupervised Machine Learning

Horvat

Jović

Burnik³

2022

Applied Sciences

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Affective video recommender systems: A survey

Wang

Zhao

2022

Front. Neurosci.

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Traditional video recommendation provides the viewers with customized media content according to their historical records (e.g., ratings, reviews). However, such systems tend to generate terrible results if the data is insufficient, which leads to a cold-start problem. An affective video recommender system (AVRS) is a multidiscipline and multimodal human-robot interaction (HRI) system, and it incorporates physical, physiological, neuroscience, and computer science subjects and multimedia resources, including text, audio, and video. As a promising research domain, AVRS employs advanced affective analysis technologies in video resources; therefore, it can solve the cold-start problem. In AVRS, the viewers’ emotional responses can be obtained from various techniques, including physical signals (e.g., facial expression, gestures, and speech) and internal signals (e.g., physiological signals). The changes in these signals can be detected when the viewers face specific situations. The physiological signals are a response to central and autonomic nervous systems and are mostly involuntarily activated, which cannot be easily controlled. Therefore, it is suitable for reliable emotion analysis. The physical signals can be recorded by a webcam or recorder. In contrast, the physiological signals can be collected by various equipment, e.g., psychophysiological heart rate (HR) signals calculated by echocardiogram (ECG), electro-dermal activity (EDA), and brain activity (GA) from electroencephalography (EEG) signals, skin conductance response (SCR) by a galvanic skin response (GSR), and photoplethysmography (PPG) estimating users’ pulse. This survey aims to provide a comprehensive overview of the AVRS domain. To analyze the recent efforts in the field of affective video recommendation, we collected 92 relevant published articles from Google Scholar and summarized the articles and their key findings. In this survey, we feature these articles concerning AVRS from different perspectives, including various traditional recommendation algorithms and advanced deep learning-based algorithms, the commonly used affective video recommendation databases, audience response categories, and evaluation methods. Finally, we conclude the challenge of AVRS and provide the potential future research directions.

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Survey: Affective Recommender Systems Techniques

Cited by 2 publications

References 59 publications

Investigation of Relationships between Discrete and Dimensional Emotion Models in Affective Picture Databases Using Unsupervised Machine Learning

Investigation of Relationships between Discrete and Dimensional Emotion Models in Affective Picture Databases Using Unsupervised Machine Learning

Affective video recommender systems: A survey

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