Large-scale music tag recommendation with explicit multiple attributes

Social tagging can provide rich semantic information for largescale retrieval in music discovery. Such collaborative intelligence, however, also generates a high degree of tags unhelpful to discovery, some of which obfuscate critical information. Towards addressing these shortcomings, tag recommendation for more robust music discovery is an emerging topic of significance for researchers. However, current methods do not consider diversity of music attributes, often using simple heuristics such as tag frequency for filtering out irrelevant tags. Music attributes encompass any number of perceived dimensions, for instance vocalness, genre, and instrumentation. Many of these are underrepresented by current tag recommenders. We propose a scheme for tag recommendation using Explicit Multiple Attributes based on tag semantic similarity and music content. In our approach, the attribute space is explicitly constrained at the outset to a set that minimizes semantic loss and tag noise, while ensu...

• Type: Journal
• Year: 2010
• Where: MM
• Authors: Zhendong Zhao, Xinxi Wang, Qiaoliang Xiang, Andy M. Sarroff, Zhonghua Li, Ye Wang
• Keywords: Explicit Multiple, MM 2010, Multimedia, Music, Tag Recommendation