Music recommendation via heterogeneous information graph embedding

Dongjing WANG, Guandong XU, Shuiguang DENG

Research output: Chapter in Book/Report/Conference proceedingChapters

18 Citations (Scopus)

Abstract

Traditional music recommendation techniques suffer from limited performance due to the sparsity of user-music interaction data, which is addressed by incorporating auxiliary information. In this paper, we study the problem of personalized music recommendation that takes different kinds of auxiliary information into consideration. To achieve this goal, a Heterogeneous Information Graph (HIG) is first constructed to encode different kinds of heterogeneous information, including the interactions between users and music pieces, music playing sequences, and the metadata of music pieces. Based on HIG, a Heterogeneous Information Graph Embedding method (HIGE) is proposed to learn the latent low-dimensional representations of music pieces. Then, we further develop a context-aware music recommendation method. Extensive experiments have been conducted on real-world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse datasets. Copyright © 2017 IEEE.

Original languageEnglish
Title of host publicationProceedings of 2017 International Joint Conference on Neural Networks, IJCNN 2017
Place of PublicationUSA
PublisherIEEE
Pages596-603
ISBN (Electronic)9781509061815
DOIs
Publication statusPublished - Jun 2017

Citation

Wang, D., Xu, G., & Deng, S. (2017). Music recommendation via heterogeneous information graph embedding. In Proceedings of 2017 International Joint Conference on Neural Networks, IJCNN 2017,(pp. 596-603). IEEE. https://doi.org/10.1109/IJCNN.2017.7965907

Fingerprint

Dive into the research topics of 'Music recommendation via heterogeneous information graph embedding'. Together they form a unique fingerprint.