Preface to the Special Issue on user modeling for personalized interaction with music

Tkalčič, Marko; Schedl, Markus; Knees, Peter

doi:10.1007/s11257-020-09264-6

Cited by 2 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, with the rapid development of mobile communication, people are more inclined to use mobiles for entertainment and communication, and the recommendation system based on social networks has obvious market prospects. Social networks contain a huge amount of user information, and people are usually willing to share items with their friends, and using this relationship helps to increase the recommendation success rate [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Music Personalized Label Clustering and Recommendation Visualization

Huo

2021

Complexity

View full text Add to dashboard Cite

With the advent of big data, the performance of traditional recommendation algorithms is no longer enough to meet the demand. Most people do not leave too many comments and other data when using the application. In this case, the user data are too scattered and discrete, with obvious data sparsity problems. First, this paper describes the main ideas and methods used in current recommendation systems and summarizes the areas that need attention and consideration. Based on these algorithms and based on the user history data information and music data information that can be found now, the paper aims to build a personalized music recommendation system based on directed tags, which can provide basic music services to users and push them personalized music recommendation lists. Then, the collaborative filtering algorithm based on tags is introduced. Usually this method uses discrete tags, and the user tags and music tags are juxtaposed and leveled with each other, which does not reflect the importance and ranking order relationship of each tag and does not reflect the cognitive sequence of users when they listen to and annotate music. In order to improve this problem and increase the accuracy of recommendations, the user-tag and music-tag data are correlated through the tag sequence of tag and music-tag data are correlated and modeled analytically, and feature directed graphs are constructed.

show abstract

Section: Introductionmentioning

confidence: 99%