A novel video recommendation system based on efficient retrieval of human actions

Ramezani, Mohsen

doi:10.1016/j.physa.2016.03.101

Cited by 15 publications

(11 citation statements)

References 54 publications

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“…To address challenges like the cold start problem and limited content analysis, automatic extraction of features has proven effective in representing video content for recommendation (Luo et al, 2008 ; Ramezani and Yaghmaee, 2016 ; Lee and Abu-El-Haija, 2017 ; Hazrati and Elahi, 2021 ; Rimaz et al, 2021 ). The selection of features impacts recommendation quality, with different multimedia features showing varying effectiveness across video domains.…”

Section: Discussionmentioning

confidence: 99%

“…For content-based recommendation, this approach is used to identify similar items to a seed or user preferences represented in the same embedding space. Any kind of content representation can be taken into account (see Section 3.1), and the approach is applicable to a variety of domains, e.g., for clustering sports videos based on recognized human actions (Ramezani and Yaghmaee, 2016 ) or using the identified topic of videos (Wu et al, 2008 ).…”

Section: Video Recommender Systemsmentioning

confidence: 99%

“…A popular clustering approach for video recommendation is k-Means (Wu et al, 2008 ; Deldjoo et al, 2016 , 2018b ; Ramezani and Yaghmaee, 2016 ), which is an iterative algorithm that assigns items to one of k clusters, such that the distance between the centroid (cluster center) and the item is minimized, given a distance metric (Jannach et al, 2011a ). For a standard recommendation approach, clustering can be involved to identify the most similar cluster based on a user's context and recommend videos from that cluster that the user has not seen yet.…”

Section: Video Recommender Systemsmentioning

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

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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: Video Recommender Systemsmentioning

confidence: 99%

See 1 more Smart Citation

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

Lubos,

Felfernig,

Tautschnig

2023

Front. Big Data

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“…Five professional weightlifters were helped in this issue, as shown in table 1 their anthropometric characters and the speed and their record weight is comprised [10,11]. The image processing toolbox in MATLAB was used to find out the above-mentioned parameters for each joint during three pre-processing, processing and post-processing stages [12].…”

Section: Methodsmentioning

confidence: 99%

Introducing an Image Processing Method for Evaluation of Clean and Jerk Style in Weightlifting using Measured Biomechanical Parameters

Almasi¹

2018

IJCA

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Weightlifting is one of the high-medal sports in the Olympic Games. Although one of the most critical weaknesses of this sport is the lack of using new method of technology to improve the quality of player's performance and also decline the referee mistakes. The aim of this paper is to use Image processing to figure out the clinical parameters to examine and measuring the angular kinematic and kinetic features of clean and jerk style in weightlifting. And using this approach for defining the standard pattern for distinguishing the difference between correct and wrong movements that can be used in talent identification.

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“…In the equations, ε is a stop threshold, m is a fuzziness exponent, and || * || is a norm expressing the similarity between any measured datum and the centroid. Regarding both ratings and additional items' information, several works have employed the fuzzy c-means clustering [11,38,40,66,73,87,101,103,128,129,131,141,142], and also similar approaches such as relational fuzzy subtractive clustering [121], co-clustering [45,49,114,133], picture fuzzy clustering [123], folksonomy-focused intuitionistic fuzzy agglomerative hierarchical clustering [43], fuzzy geographical clustering [119], linear fuzzy clustering [48], and other fuzzy clustering approaches [18,29,35,39,61,64,143].…”

Section: Maementioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

152

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Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

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A novel video recommendation system based on efficient retrieval of human actions

Cited by 15 publications

References 54 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

Introducing an Image Processing Method for Evaluation of Clean and Jerk Style in Weightlifting using Measured Biomechanical Parameters

Fuzzy Tools in Recommender Systems: A Survey

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