Enhanced Books Recommendation Using Clustering Techniques and Knowledge Graphs

Valdiviezo-Díaz, Priscila; Chicaiza, Janneth

doi:10.1007/978-3-031-24985-3_7

Cited by 1 publication

(2 citation statements)

References 27 publications

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“…The calculation of Euclidean distance is shown below, which refers to the distance between 2 points in a ndimensional Euclidean space . The code of recommending system is shown below [9].…”

Section: Figure 3 the Graph Of Elbow Methods Resultsmentioning

confidence: 99%

“…The paper "Enhanced Books Recommendation Using Clustering Techniques and Knowledge Graphs" discusses the use of clustering techniques in recommendation systems. This paper is particularly relevant as it demonstrates the application of clustering techniques, including the use of Euclidean distance, in a recommendation system [9].…”

Section: Sub-section 2: Recommending Systemmentioning

confidence: 99%

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Investigating the working principle of the recommending system and designing a personal music recommending system

2024

ACE

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This paper, Investigating the Working Principle of the Recommending System and Designing a Personal Music Recommending System, delves into the intricacies of recommendation systems, with a specific focus on music. It begins by exploring the fundamental principles that underpin recommendation systems.The latter part of the paper presents the design of a novel personal music recommendation system. This system leverages the results of K-means clustering to provide faster personalized music recommendations. It considers various auditory factors such as valence (the musical positiveness conveyed by a track), duration (the length of the track), and energy (a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity). These factors are calculated by Spotify and made available in an open data set. This research contributes significantly to the field by providing a deeper understanding of recommendation systems and proposing an innovative solution for personalized music recommendations. The novelty of this system lies in its use of K-means clustering and the specific auditory factors it considers, which sets it apart from existing systems.

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“…The calculation of Euclidean distance is shown below, which refers to the distance between 2 points in a ndimensional Euclidean space . The code of recommending system is shown below [9].…”

Section: Figure 3 the Graph Of Elbow Methods Resultsmentioning

confidence: 99%

Section: Sub-section 2: Recommending Systemmentioning

confidence: 99%

Investigating the working principle of the recommending system and designing a personal music recommending system

2024

ACE

View full text Add to dashboard Cite

show abstract

Enhanced Books Recommendation Using Clustering Techniques and Knowledge Graphs

Cited by 1 publication

References 27 publications

Investigating the working principle of the recommending system and designing a personal music recommending system

Investigating the working principle of the recommending system and designing a personal music recommending system

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