In Search of the Goosebump Factor - A Blueprint for Emotional Music Recommenders (15/11/07)
Music Information Retrieval (MIR) as an interdisciplinary research discipline achieved impressive progress over the last decade. Pandora, Last.fm or MyStrands are successful commercial webservices offering previously unavailable convenience for customers. Although such systems compute personalised recommendations based on relevance feedback on top of content-based, expert-based or community metadata, the embedding of emotional context is still a challenge. In my talk I will sketch a blueprint towards an architecture of an emotional music recommender in order to solve the abovementioned problem. The approach is in its infancy but we have already the core ingredients developed. Lifestream aggregation from Web2.0 platforms and the analysis of blog postings will be aligned with the analysis of song lyrics. Furthermore we propose an open Web2.0 platform in order to collect personal descriptions of "goosebump sensations" when listening to music. This collection will be available to researchers in the field to serve as a common ground for training emotional classifiers.