Paper Info
Title
A novel music recommender by discovering preferable perceptual-patterns from music pieces
Abstract
Nowadays, advanced information and communication technologies ease the access of music pieces. However, it is still hard for the users to find what she/he prefers from a huge amount of music works. To solve this problem, most music recommenders based on collaborative filtering (called CF) utilize the rating logs to predict the user's preference. Unfortunately, CF-like recommenders cannot capture the user's preference effectively due to the gap between the complicated musical contents and diverse user preferences. To reduce the gap, in this paper, we propose a novel recommender that integrates musical contents mining and collaborative filtering to achieve high-quality music recommendation. For musical contents mining, the proposed perceptual patterns derived by Two-stage clustering are adopted as a kind of musical genes to support music recommendation. For collaborative filtering, pattern-based preference prediction can imply the user's preferred music effectively. The experimental results reveal that our proposed recommender well outperforms the existing recommenders in terms of recommendation quality.
Year
DOI
Venue
2010
10.1145/1774088.1774495
SAC
Keywords
Field
DocType
musical gene,existing recommenders,music piece,complicated musical content,preferred music,music recommendation,preferable perceptual-patterns,diverse user preference,novel music recommender,music work,high-quality music recommendation,musical contents mining,data mining,collaborative filtering,information and communication technology
Recommender system,Collaborative filtering,Information retrieval,Musical,Computer science,Information and Communications Technology,Cluster analysis,Multimedia,Perception
Conference
Citations 
PageRank 
References 
2
0.42
11
Authors
3
Name
Order
Citations
PageRank
Ja-Hwung Su132924.53
Hsin-Ho Yeh21337.26
Vincent S. Tseng32923161.33