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 2 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

2024

ACE

View full text Add to dashboard Cite

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.

show abstract

“…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

“…With the specification that the second proposed approach, including dimension reduction, decreases the margin of error significantly. In addition to the work abrogated in [45], which consists in using two hybrids clustering algorithms based on sequences and hierarchy, for recommendation of e-commerce web pages. This recommendation method based on clustering has shown significant results.…”

Section: B Literary Reviewmentioning

confidence: 99%

“…By reverting to previous clustering methods, the obtained recommendation results are stable and improved. Still in the field of education, a system for recommending books along with their descriptions and metadata based on an RDF knowledge graph is designed in [45]. The initially followed approach focuses on forming book clusters via the K-means algorithm, and the next step involves predicting and scoring books before making collaborative filterbased recommendations.…”

Section: B Literary Reviewmentioning

confidence: 99%

Agent-based approach for the recommendation and unsupervised classification of enterprise services in the cloud

Bourenane¹,

Houari²,

Taghezout³

2023

IJCDS

View full text Add to dashboard Cite

The main objective of this study is to create a collaborative parallel environment, which supports the unsupervised classification and the filtration of an important volume of information. The proposed approach consists in the integration of an agent-based system composed of five reactive agents to assist the recommendation of the stored services in the cloud and to cluster these services through a new improved K-means as well. The conducted experiments and evaluations of the different approaches and measures, such as: Euclidean distance, Manhattan distance and Cosine similarity, show that the proposed approach of the unsupervised classification improve the within cluster sum of squares (WCSS), which facilitates the access to personalized and relevant services requested in a very improved response time, especially through migration to the cloud using agents.

show abstract

Enhanced Books Recommendation Using Clustering Techniques and Knowledge Graphs

Cited by 2 publications

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

Agent-based approach for the recommendation and unsupervised classification of enterprise services in the cloud

Contact Info

Product

Resources

About