Paper Info
Title
A survey of music similarity and recommendation from music context data
Abstract
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-context-based (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), co-occurrence-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.
Year
DOI
Venue
2013
10.1145/2542205.2542206
TOMCCAP
Keywords
Field
DocType
music information retrieval,contextual data,individual music piece,music recommendation,music artist,music context data,music similarity estimation,music content,music retrieval,context-based data
Audio signal,Music information retrieval,World Wide Web,Social media,Computer science,Contextual design,Microblogging,Active listening,Lyrics,Pop music automation,Multimedia
Journal
Volume
Issue
ISSN
10
1
1551-6857
Citations 
PageRank 
References 
39
1.59
64
Authors
2
Name
Order
Citations
PageRank
Peter Knees159451.71
Markus Schedl21431117.09