MusicSense

Abstract

In this paper, we present a novel contextual music recommendation approach, MusicSense, to automatically suggest music when users read Web documents such as Weblogs. MusicSense matches music to a document's content, in terms of the emotions expressed by both the document and the music songs. To achieve this, we propose a generative model - Emotional Allocation Modeling - in which a collection of word terms is considered as generated with a mixture of emotions. This model also integrates knowledge discovering from a Web-scale corpus and guidance from psychological studies of emotion. Music songs are also described using textual information extracted from their meta-data and relevant Web pages. Thus, both music songs and Web documents can be characterized as distributions over the emotion mixtures through the emotional allocation modeling. For a given document, the songs with the most matched emotion distributions are finally selected as the recommendations. Preliminary experiments on Weblogs show promising results on both emotion allocation and music recommendation.