Listener-aware Music Recommendation from Sensor and Social Media Data

Markus Schedl

Listener-aware Music Recommendation from Sensor and Social Media Data

Institute of Computational Perception

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Music recommender systems are lately seeing a sharp increase in popularity due to many novel commercial music streaming services. Most systems, however, do not decently take their listeners into account when recommending music items. In this note, we summarize our recent work and report our latest findings on the topics of tailoring music recommendations to individual listeners and to groups of listeners sharing certain characteristics. We focus on two tasks: context-aware automatic playlist generation (also known as serial recommendation) using sensor data and music artist recommendation using social media data.

Original language	English
Title of host publication	Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Number of pages	4
Publication status	Published - 2015

Fields of science

202002 Audiovisual media
102 Computer Sciences
102001 Artificial intelligence
102003 Image processing
102015 Information systems

JKU Focus areas

Computation in Informatics and Mathematics
Engineering and Natural Sciences (in general)

Cite this

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title = "Listener-aware Music Recommendation from Sensor and Social Media Data",

abstract = "Music recommender systems are lately seeing a sharp increase in popularity due to many novel commercial music streaming services. Most systems, however, do not decently take their listeners into account when recommending music items. In this note, we summarize our recent work and report our latest findings on the topics of tailoring music recommendations to individual listeners and to groups of listeners sharing certain characteristics. We focus on two tasks: context-aware automatic playlist generation (also known as serial recommendation) using sensor data and music artist recommendation using social media data.",

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year = "2015",

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booktitle = "Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases",

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AB - Music recommender systems are lately seeing a sharp increase in popularity due to many novel commercial music streaming services. Most systems, however, do not decently take their listeners into account when recommending music items. In this note, we summarize our recent work and report our latest findings on the topics of tailoring music recommendations to individual listeners and to groups of listeners sharing certain characteristics. We focus on two tasks: context-aware automatic playlist generation (also known as serial recommendation) using sensor data and music artist recommendation using social media data.

M3 - Conference proceedings

BT - Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

ER -