Image perception is one of the most direct ways to provide contextual information about a user concerning his/her surrounding environment; hence images are a suitable proxy for contextual recommendation. We propose a novel representation learning framework for image-based music recommendation that bridges the heterogeneity gap between music and image data; the proposed method is a key component for various contextual recommendation tasks. Preliminary experiments show that for an image-to-song retrieval task, the proposed method retrieves relevant or conceptually similar songs for input images.
@article{arxiv.1808.09198,
title = {Representation Learning for Image-based Music Recommendation},
author = {Chih-Chun Hsia and Kwei-Herng Lai and Yian Chen and Chuan-Ju Wang and Ming-Feng Tsai},
journal= {arXiv preprint arXiv:1808.09198},
year = {2018}
}