Representation Learning for Image-based Music Recommendation

TL;DR

This paper introduces a novel representation learning framework that uses images to improve music recommendation by bridging the gap between image and music data, demonstrated through image-to-song retrieval tasks.

Contribution

It presents a new method for image-based music recommendation that effectively connects visual and auditory data for contextual suggestions.

Findings

Successfully retrieves relevant songs for input images

Bridges heterogeneity gap between image and music data

Enhances contextual music recommendation capabilities

Abstract

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.