Human Emotion Prediction Analysis on Post-COVID-19 Crisis in Digital Media Using Deep Learning

Agarwal, Nikita; Dutta, Ritam

doi:10.1007/978-981-19-2004-2_36

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The media mode and information carrier of Internet media are different from traditional media such as telephone, record, film, radio, television and mobile phone communication [1,2]. The flexibility and maneuverability of Internet media, which is the main development platform of future media, are beyond the reach of traditional media [3][4][5][6][7]. Video is an important carrier of various forms of information on the Internet.…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Liu¹

2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Video is an important carrier of all kinds of information on the Internet. More and more students choose to watch online learning videos from online learning platforms with various forms of intelligent terminals. To allow students to get the videos which they really need and are interested in from the mass videos, it is necessary to improve the existing personalized recommendation algorithm. Content-based recommendation method, collaborative filtering-based recommendation method and hybrid recommendation method often have the problems of loss of feature information or cocoon. To this end, this paper studies the personalized recommendation service of media educational resources based on multi-dimensional user features. This paper constructs a multi-feature candidate set based on student-video interaction, extracts and splices category feature, label feature and semantic feature of Internet media educational videos, and generates the representation of students’ learning preference feature. Based on the improved forward and backward recurrent neural network, this study constructs a recommendation model of Internet media educational videos oriented to students' preference points. The experimental result verifies the validity of the model.

show abstract

Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Liu¹

2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

Human Emotion Prediction Analysis on Post-COVID-19 Crisis in Digital Media Using Deep Learning

Cited by 1 publication

References 9 publications

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Contact Info

Product

Resources

About