2016
DOI: 10.1016/j.eswa.2016.09.019
|View full text |Cite
|
Sign up to set email alerts
|

A collaborative filtering method for music recommendation using playing coefficients for artists and users

Abstract: The great quantity of music content available online has increased interest in music recommender systems. However, some important problems must be addressed in order to give reliable recommendations. Many approaches have been proposed to deal with cold-start and first-rater drawbacks; however, the problem of generating recommendations for gray-sheep users has been less studied. Most of the methods that address this problem are content-based, hence they require item information that is not always available. Ano… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
30
0
2

Year Published

2017
2017
2023
2023

Publication Types

Select...
6
3
1

Relationship

0
10

Authors

Journals

citations
Cited by 99 publications
(32 citation statements)
references
References 26 publications
0
30
0
2
Order By: Relevance
“…Furthermore, Rodríguez, Torres, and Garza () proposed an approach that tackles contact recommendation in Twitter by means of fuzzy logic. Sánchez‐Moreno, González, Vicente, Batista, and García () applied the collaborative filtering approach to music recommendations for both rating predation and item recommendation. Ezghari, Zahi, and Zenkouar () introduced the novel fuzzy nearest neighbour classification method and described the domain features by FSs.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Furthermore, Rodríguez, Torres, and Garza () proposed an approach that tackles contact recommendation in Twitter by means of fuzzy logic. Sánchez‐Moreno, González, Vicente, Batista, and García () applied the collaborative filtering approach to music recommendations for both rating predation and item recommendation. Ezghari, Zahi, and Zenkouar () introduced the novel fuzzy nearest neighbour classification method and described the domain features by FSs.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Materi dengan rating tinggi akan menjadi rekomendasi bagi audiens untuk mendapatkan ilmu/pengetahuan sesuai yang diinginkannya. Penggunaan sistem rekomendasi ini menjadi sangat penting guna memfilter sejumlah besar materi yang tersedia menjadi beberapa materi yang berkualitas [11]- [13]. Tentu dalam proses filter tersebut digunakan sistem rekomendasi yang pantas untuk mendapatkan hasil yang sesuai.…”
Section: Metode Penelitianunclassified
“…However, some important problems, such as the difficulty of extracting content information from music, must be addressed in order to give reliable recommendations. Sánchez-Moreno et al [25] propose a recommendation method based on playing coefficients to deal with gray sheep and sparsity problems without needing user attributes, content data, and explicit ratings from users, and the proposal is proved to outperform the methods that make use of user attributes. Meanwhile, content personalization is a long-standing problem for online news services.…”
Section: Recommendation Research On E-resource Servicementioning
confidence: 99%