Title

Efficient Karaoke Song Recommendation Via Multiple Kernel Learning Approximation

Abstract

Online karaoke allows users to practice singing and distribute recordings. Different from traditional music recommendation, online karaoke need to consider users' vocal competence besides their tastes. In this paper, we develop a karaoke recommender system by taking into account vocal competence. Alone this line, we propose a joint modeling method named MKLA by adopting bregman divergence as the regularizer in the formulation of multiple kernel learning. Specially, we first extract users' vocal ratings from their singing recordings. Due to an ever-increasing number of recordings, the evaluations in large-scale kernel matrix may cost lots of time and internal storage. Therefore, we propose a sample compression method to eliminate users' vocal ratings, exploit an MKL method, and learn the latent features of the vocal ratings. These latent features are simultaneously fed into a bregman divergence and then we use the trained classifier to predict the overall rating of a user with respect to a song. Enhanced by this new formulation, we develop the SMO method for optimizing the MKLA dual and present a theoretical analysis to show the lower bound of our method. With the estimated model, we compute the matching degree of users and songs in terms of pitch, volume and rhythm and recommend songs to users. Finally, we conduct extensive experiments with online karaoke data. The results demonstrate the effectiveness of our method.

Department(s)

Computer Science

Comments

This research was partially supported by grants from the National Key Research and Development Program of China (Grant No. 2016YFB1000904), the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010) and the Fundamental Research Funds for the Central Universities of China (Grant No. WK2350000001).Professor Hui Xiong is partially supported by National Natural Science Foundation of China (71531001).

Keywords and Phrases

Computer applications; Neural networks; Bregman divergences; Internal storage; Kaorake recommendation; Kernel matrices; Multiple Kernel Learning; Music recommendation; Sample compression; Singing competence; Digital storage; Article; conceptual framework; Kernel method; Machine learning; Mathematical computing; Mathematical model; Online system; Pitch; Priority journal; Singing

International Standard Serial Number (ISSN)

0925-2312

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Elsevier, All rights reserved.

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