Credible user-review incorporated collaborative filtering for video recommendation system

Mahadevan, Anbazhagan; Arock, Michael

doi:10.1109/iss1.2017.8389433

Cited by 11 publications

(5 citation statements)

References 4 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, 2015Gao 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%

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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%

“…As sparse user ratings can negatively impact the recommendation quality, the usage of sentiment analysis on free-text reviews is suggested in Mahadevan and Arock (2017) to address this issue. NLP techniques are used to deduce numerical ratings from credible reviews, which are then used in the recommendation process.…”

Section: Self-supervised Learningmentioning

confidence: 99%

Reviews in Recommender Systems: 2022

2024

Frontiers Research Topics

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show abstract

“…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%

“…As sparse user ratings can negatively impact the recommendation quality, the usage of sentiment analysis on free-text reviews is suggested in Mahadevan and Arock ( 2017 ) to address this issue. NLP techniques are used to deduce numerical ratings from credible reviews, which are then used in the recommendation process.…”

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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“…Because it could be on the other hand, the user judges poorly in one thing but judges both in some ways, so the user prefers good judgment [14] and uses fewer reviews. Today's assessment system not only allows consumers to read and write reviews about products but also to check the credibility of reviews [15]. A widely used recommendation system usually based on similarities between active users and other users [16].…”

Section: Introductionmentioning

confidence: 99%

GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM

Awangga

Pane

Wijayanti

2019

emitter

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A rating system or reviews are generally used to assist in making decisions. Rating system widely used as a technique in the recommendation of one of them used by the customer, as in determining the resort to be used. However, the credibility of the rating looks vague because the rating could only represent some points of service. So that customer preference with each other is very different. Personalized recommendation systems offer more personalized advice, precisely knowing the preferences or tastes of the customers. Especially for customers who have a transaction history or reservation as at their resorts provide good information used by managers to design a recommendation model for their customers. In this study aims to create a model of resort recommendations based on a rating of frequency. This frequency is the number of resort use by the customer within the specified time frame. With the frequency can represent the preferences of customers. The RFM method is used to measure the reservation frequency value of the customer. The K-Means method is used to categorize customer data with its frequency and classify the type of resort. Recommendation resort to the customer based on the dominant use in one of the resort types. The recommended type of resort based on the similarity between the types of resorts used with other types of resorts.

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Credible user-review incorporated collaborative filtering for video recommendation system

Cited by 11 publications

References 4 publications

Reviews in Recommender Systems: 2022

Reviews in Recommender Systems: 2022

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

GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM

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