Exploiting Relevant Context with Soft-Rough Sets in Context-Aware Video Recommender Systems

Abbas, Syed Manzar; Alam, Khubaib Amjad

doi:10.1109/fuzz-ieee.2019.8859003

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…By adding context information to video recommenders, the challenge of changing user interests based on spatial or temporal context can be addressed. These systems incorporate information about when and where users consume videos, allowing them to provide more relevant and useful recommendations, ultimately enhancing the overall user experience (Abbas and Amjad Alam, 2019;.…”

Section: Discussionmentioning

confidence: 99%

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Reviews in Recommender Systems: 2022

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Discussionmentioning

confidence: 99%

“…Addressing the challenge of identifying suitable contexts for videos watched by diverse users, the usage of Soft-Rough sets was proposed in Abbas and Amjad Alam (2019). While traditional rough sets handle incomplete or uncertain data by extracting patterns, they struggled to establish decision rules for videocontext detection.…”

Section: Context-awarenessmentioning

confidence: 99%

Reviews in Recommender Systems: 2022

2024

Frontiers Research Topics

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show abstract

Section: Discussionmentioning

confidence: 99%

“…Addressing the challenge of identifying suitable contexts for videos watched by diverse users, the usage of Soft-Rough sets was proposed in Abbas and Amjad Alam ( 2019 ). While traditional rough sets handle incomplete or uncertain data by extracting patterns, they struggled to establish decision rules for video-context detection.…”

Section: Video Recommender Systemsmentioning

confidence: 99%

An overview of video recommender systems: state-of-the-art and research issues

Lubos,

Felfernig,

Tautschnig

2023

Front. Big Data

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Video platforms have become indispensable components within a diverse range of applications, serving various purposes in entertainment, e-learning, corporate training, online documentation, and news provision. As the volume and complexity of video content continue to grow, the need for personalized access features becomes an inevitable requirement to ensure efficient content consumption. To address this need, recommender systems have emerged as helpful tools providing personalized video access. By leveraging past user-specific video consumption data and the preferences of similar users, these systems excel in recommending videos that are highly relevant to individual users. This article presents a comprehensive overview of the current state of video recommender systems (VRS), exploring the algorithms used, their applications, and related aspects. In addition to an in-depth analysis of existing approaches, this review also addresses unresolved research challenges within this domain. These unexplored areas offer exciting opportunities for advancements and innovations, aiming to enhance the accuracy and effectiveness of personalized video recommendations. Overall, this article serves as a valuable resource for researchers, practitioners, and stakeholders in the video domain. It offers insights into cutting-edge algorithms, successful applications, and areas that merit further exploration to advance the field of video recommendation.

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“…Todas essas informações podem ser denominadas avaliações (ou ratings), e são a fonte primária de informação dos sistemas de recomendação (AGGARWAL, 2016). No entanto, a maioria dos sistemas de recomendação recentes já utiliza também outras fontes de informação externa, como revisões de usuários na forma de textos, informação contextual e imagens (BARAL et al, 2018;SATO et al, 2018;ABBAS;ALAM, 2019;CHENG et al, 2019;TONON et al, 2019;BLANCO-MALLO et al, 2020;SUNDERMANN et al, 2020). Na seção seguinte, serão apresentados como todos esses dados/informações podem ser modelados nos sistemas de recomendação.…”

Section: Fundamentação Téoricaunclassified

Geração de recomendações interpretáveis em sistemas de recomendação utilizando contexto

Tonon¹

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Primeiramente, gostaria de agradecer aos meus pais, Sandra e Renato, por todo o amor, carinho, apoio e suporte prestados a mim, do momento em que eu nasci até este momento. Obrigado por entenderem todas as vezes que não estive disponível ou que não pude lhes a atenção que mereciam. Sei que vocês abdicaram muito para que eu pudesse me dedicar aos estudos, e agradeço por terem aberto todas as portas nesse sentido. Se hoje eu estou aqui, devo tudo a vocês.Agradeço também à minha irmã, Carol, pelo apoio e companheirismo que sempre tivemos. Sempre fomos muito próximos um do outro, e acredito que isso contribuiu muito para que eu me tornasse quem eu sou hoje. Por mais que os compromissos e as correrias do dia-a-dia nos tenham afastado um pouco, conte comigo para o que for preciso. Sua enorme dedicação aos seus objetivos, serviram como inspiração para mim.Estendo meus agradecimentos a todos os outros familiares que estiverem presentes durante o meu crescimento e formação. Em especial, gostaria de agradecer à minha tia Madalena, que com seu jeito único de ser, sempre me fez sentir muito amado. Os cafés da tarde e tantas conversas serviram como um refugio às preocupações da pós-graduação. Além disso, gostaria de agradecer ao meu tio Márcio e a sua esposa, Érika, que também sempre estiveram comigo, desde a minha infância até os dias de hoje. Resumo

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Exploiting Relevant Context with Soft-Rough Sets in Context-Aware Video Recommender Systems

Cited by 10 publications

References 12 publications

Reviews in Recommender Systems: 2022

Reviews in Recommender Systems: 2022

An overview of video recommender systems: state-of-the-art and research issues

Geração de recomendações interpretáveis em sistemas de recomendação utilizando contexto

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