Hybrid Recommendation of Movies Based on Deep Content Features

Kvifte, Tord; Elahi, Mehdi; Trattner, Christoph

doi:10.1007/978-3-031-14135-5_3

Cited by 4 publications

(8 citation statements)

References 37 publications

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“…Hybrid video recommenders combine different methods to overcome individual limitations. To address cold start for new users, hybrids merge CF with CBF by enriching user profiles from other sources (Cui et al, 2014 ; Vizine Pereira and Hruschka, 2015 ) or augmenting items with content descriptions (Öztürk and Kesim Cicekli, 2011 ; Wang et al, 2015 , 2021 ; Gao et al, 2017 ; Mahadevan and Arock, 2017 ; Wei et al, 2017 ; Liu et al, 2019b ; Kvifte et al, 2021 ). The latter is particularly helpful in mitigating the sparsity of user ratings.…”

Section: Discussionmentioning

confidence: 99%

“…To improve the accuracy of recommendations in the presence of cold start and sparse ratings different approaches were suggested. In Kvifte et al ( 2021 ), the usage of aggregated content data (visual features and word frequency in subtitles) and user ratings to predict recommendations via matrix factorization was presented. In Wang et al ( 2021 ), a two-tower model is proposed to improve cold starts.…”

Section: Video Recommender Systemsmentioning

confidence: 99%

“…Advertisement Textual Kaklauskas et al, 2018;Kim et al, 2021Education Textual Chantanurak et al, 2016Kimoto et al, 2016;Tavakoli et al, 2020;Leite et al, 2022 Movies and Series Aural Deldjoo et al, 2018a;Rimaz et al, 2021;Chakder et al, 2022;Pingali et al, 2022;Mondal et al, 2023 Textual Öztürk andKesim Cicekli, 2011;Zhu et al, 2013;Vizine Pereira and Hruschka, 2015;Wang et al, 2015Wang et al, , 2021Gomez-Uribe and Hunt, 2016;Elahi et al, 2017;Lu et al, 2017;Wei et al, 2017;Liu et al, 2019b;Kvifte et al, 2021;Zhuo et al, 2021;Chakder et al, 2022;Pingali et al, 2022;Mondal et al, 2023Visual Zhu et al, 2013Deldjoo et al, 2016Deldjoo et al, , 2018aElahi et al, 2017Elahi et al, , 2020Elahi et al, , 2021Hazrati and Elahi, 2021;Kvifte et al, 2021;Wang et al, 2021;Chakder et al, 2022;Pingali et al, 2022;Mondal et al, 2023 News Aural Luo et al, 2008Textual Luo et al, 2008Hopfgartner and…”

Section: Domain Features Referencesmentioning

confidence: 99%

“…Matrix factorization Cui et al, 2014;Roy and Guntuku, 2016;Kvifte et al, 2021;Wang et al, 2021 Deep neural networks Wang et al, 2015;Gao et al, 2017;Wei et al, 2017;Liu et al, 2019a;Chen et al, 2021 Multi-task learning Ma et al, 2019;Zhao et al, 2019;Tang et al, 2020;Zhuo et al, 2021;Song et al, 2023 Further approaches Öztürk and Kesim Cicekli, 2011;Vizine Pereira and Hruschka, 2015;Abbas et al, 2017;Liu et al, 2019b;Kim et al, 2021 To handle the problem of unavailable explicit ratings, Interest Preferences of Categories (IPoC) can be deduced as implicit ratings from user logs (Chen et al, 2019). View times of short videos are used to determine ratings, reflecting user interest in specific categories through weighted video consumption times.…”

Section: Type Referencesmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Discussionmentioning

confidence: 99%

Section: Video Recommender Systemsmentioning

confidence: 99%

Section: Domain Features Referencesmentioning

confidence: 99%

Section: Type Referencesmentioning

confidence: 99%

See 2 more Smart Citations

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

Lubos,

Felfernig,

Tautschnig

2023

Front. Big Data

View full text Add to dashboard Cite

show abstract

“…Advertisement Textual Kaklauskas et al, 2018;Kim et al, 2021Education Textual Chantanurak et al, 2016Kimoto et al, 2016;Tavakoli et al, 2020;Leite et al, 2022 Movies and Series Aural Deldjoo et al, 2018a;Rimaz et al, 2021;Chakder et al, 2022;Pingali et al, 2022;Mondal et al, 2023 Textual Öztürk andKesim Cicekli, 2011;Vizine Pereira and Hruschka, 2015;Wang et al, 2015Elahi et al, 2017;Lu et al, 2017;Wei et al, 2017;Liu et al, 2019b;Kvifte et al, 2021;Zhuo et al, 2021;Chakder et al, 2022;Pingali et al, 2022;Mondal et al, 2023Visual Zhu et al, 2013Deldjoo et al, 2016Deldjoo et al, , 2018aElahi et al, 2017Elahi et al, , 2020Elahi et al, , 2021Hazrati and Elahi, 2021;Kvifte et al, 2021;Chakder et al, 2022;Pingali et al, 2022;Mondal et al, 2023 News Aural Luo et al, 2008Textual Luo et al, 2008Hopfgartner andJose, 2010 Visual Luo et al, 2008 Social Networks Aural Mei et al, 2007Mei et al, , 2011…”

Section: Domain Features Referencesmentioning

confidence: 99%

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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Movie Recommendation System Using Composite Ranking

Aashal

Mehta

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Hybrid Recommendation of Movies Based on Deep Content Features

Cited by 4 publications

References 37 publications

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

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

Reviews in Recommender Systems: 2022

Movie Recommendation System Using Composite Ranking

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