In this survey article, we give an overview of methods for music similarity estimation and music recommendation based on music context data. Unlike approaches that rely on music content and have been researched for almost two decades, music-contextbased (or contextual) approaches to music retrieval are a quite recent field of research within music information retrieval (MIR). Contextual data refers to all music-relevant information that is not included in the audio signal itself. In this article, we focus on contextual aspects of music primarily accessible through web technology. We discuss different sources of context-based data for individual music pieces and for music artists. We summarize various approaches for constructing similarity measures based on the collaborative or cultural knowledge incorporated into these data sources. In particular, we identify and review three main types of context-based similarity approaches: text-retrieval-based approaches (relying on web-texts, tags, or lyrics), cooccurrence-based approaches (relying on playlists, page counts, microblogs, or peer-to-peer-networks), and approaches based on user ratings or listening habits. This article elaborates the characteristics of the presented context-based measures and discusses their strengths as well as their weaknesses.
An overview about techiques for music recommendation and similarity based on music contextual data is given in REF .
A survey of music similarity and recommendation from music context data
TOMCCAP
Computer Science