EmoWare: A Context-Aware Framework for Personalized Video Recommendation Using Affective Video Sequences

Tripathi, Abhishek; Ashwin, T. S.; Guddeti, Ram Mohana Reddy

doi:10.1109/access.2019.2911235

Cited by 31 publications

(15 citation statements)

References 61 publications

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“…Emotions play an important role in users’ selection and consumption of video content [ 1 ]. Recognizing the emotions of users while they watch videos freely in indoor and outdoor environments can enable customization and personalization of video content [ 2 , 3 ]. Although previous work has focused on emotion recognition for video watching, they are typically restricted to static, desktop environments [ 1 , 4 , 5 ], and focus on recognizing one emotion per video stimuli [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Zhang

Ali

Wang³

et al. 2020

Sensors

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Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

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Section: Introductionmentioning

confidence: 99%

CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Zhang

Ali

Wang³

et al. 2020

Sensors

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“…Reinforcement learning obtains the optimal solution to a specific problem by modeling the problem as an MDP and allowing the agents to continuously interact with the environment. The reinforcement learning models, including Q-learning [28] and state-action-reward-state-action [29], have achieved considerable contributions in the fields of optimization and decision-making. For example, Brandi et al [30] proposed a reinforcement learning model to control the supply water temperature setpoint of a heating system and obtained promising results for an office building in an integrated simulation environment.…”

Section: Reinforcement Learningmentioning

confidence: 99%

A novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization for short-term load forecasting during the COVID-19 pandemic

Zhang

Chen

Zeng

et al. 2022

IFS

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The coronavirus disease 2019 pandemic has significantly impacted the world. The sudden decline in electricity load demand caused by strict social distancing restrictions has made it difficult for traditional models to forecast the load demand during the pandemic. Therefore, in this study, a novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization is proposed for short-term load forecasting during the pandemic. First, a knowledge base containing mobility data is constructed, which can reflect the changes in visitor volume in different regions and buildings based on mobile services. Therefore, the sudden decline in load can be analyzed according to the socioeconomic behavior changes during the pandemic. Furthermore, a new transfer deep learning model is proposed to address the problem of limited mobility data associated with the pandemic. Moreover, reinforcement learning is employed to optimize the hyperparameters of the proposed model automatically, which avoids the manual adjustment of the hyperparameters, thereby maximizing the forecasting accuracy. To enhance the hyperparameter optimization efficiency of the reinforcement-learning agents, a new advance forecasting method is proposed to forecast the state-action values of the state space that have not been traversed. The experimental results on 12 real-world datasets covering different countries and cities demonstrate that the proposed model achieves high forecasting accuracy during the coronavirus disease 2019 pandemic.

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“…Furthermore, Jiang and Pardos [127] used recurrent networks to recommend quiz page. While Tripathi et al [146] used EmoWare, an emotionally intelligent video recommendation engine with context aware collaborative filtering approach for videos recommendations. Zhang et al [96] proposed restricted Boltzmann machines, while Liu et al [157] proposed Elmo model to recommend learning resources.…”

Section: Rq1 How Many Studies Supported Their Claim With Experiments and Which Datasets Were Used In The Studies?mentioning

confidence: 99%

A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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Online learning environments (OLE) are gaining popularity, including learning management systems (LMS) and massive open online courses (MOOCs), which are the best modern alternate solutions available for education in the current era. The luxury to learn irrespective of geographical and temporal restrictions makes it an attractive resource. At the start of 2020, the global pandemic enforced social distance practice worldwide, changing the work environment dynamics, leaving the people with options like online trading, work from home, and online education. The online learning environments gained particular attention in the educational sector, where users could access the online learning resources to fulfil their academic requirements during the lockdown. From massively available content such as MOOC, the learners are overwhelmed with the available choices. In this scenario, recommender systems (RS) come to the rescue to help the learner make appropriate choices for completing the enrolled course. There is tremendous scope and a multitude of opportunities available for researchers to focus on this domain. An exhaustive analysis is required to spotlight the opportunities in this realm. Various studies have been performed to provide such solutions in multiple areas of the MOOC recommendation systems (MOOCRS) such as course recommendation, learner peer recommendation, resource recommendations, to name a few. This is a compendious study into the research conducted in this area, identifying 670 articles out of 116 selected for analysis published from 2013 to 2021. It also highlights multiple areas in MOOC, where the recommendation is required, as well as technologies used by other researchers to provide solutions over time.

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EmoWare: A Context-Aware Framework for Personalized Video Recommendation Using Affective Video Sequences

Cited by 31 publications

References 61 publications

CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

A novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization for short-term load forecasting during the COVID-19 pandemic

A Systematic Mapping Review on MOOC Recommender Systems

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