MuSe Client

MuSe Server

Music Repository

UserProfile

Music Retrieval Engine

Wrapper/ Connector 1

User Profile Engine

Social Connector 1

HTML

XML

XML

HTML

Recommender Manager

REST Webservice

EvaluationFramework

Recommendation List Builder

Pluggable Recommender 1

Recommender

Re

com

me

nd

er

Inte

rfac

e

Recommender 1

Web-based User Interface

Recommendation List

AdministrationSpotify

Connector

AJAX

Spotify APIJSON

Data Repositories

Co

ord

ina

tor

User Context

Trac

k M

ana

ger

Use

r M

ana

ger

Charts

Last.fm API

We

b S

erv

ice

s

Freebase

DBpedia

Sem

an

tic

We

b

Last.fm

FaceBook

Soci

al

Ne

two

rks

RDF

RDF

Music platforms such as Spotify, Google Play Music or

Pandora make millions of songs accessible

Social Networks and the Semantic Web describe

users and items in an unprecedented granularity

New opportunities and challenges for

Music Recommenders

Challenges for development

Prototypes programmed from scratch, again and yet again!

Immediate feedback available

Manifold sources of information to combine

Challenges for evaluations

Thorough evaluations are repetitive, yet non-trivial, tasks

Many aspects are inherently subjective, e.g. serendipity

can be only investigated in vivo (with real users)

Legal issues: (1) user data is highly sensitive

(2) content-based approaches require data

Real-time monitoring Results are plotted

and can be exported

New recommender algorithms can be

plugged in comfortably

Users provide profileinformation or social identities

Play and rate recommended songs from Spotify

Conforms with state-of-the-art privacy standards

Variety of baseline algorithms for comparison included

MUSE: A Music Recommendation Management SystemMartin Przyjaciel-Zablocki Thomas Hornung Alexander Schätzle Sven Gauß Io Taxidou Georg Lausen

Department of Computer Science, University of Freiburg, Germany{zablocki, hornungt, schaetzle, gausss, taxidou, lausen} @informatik.uni-freiburg.de

MUSE (Music Sensing in a Social Context)

Music Recommendation Management System

(MRMS) for developing and evaluating

music recommendation algorithms

Takes care of all regular tasks required for

in vivo evaluations

Simple API enables support for arbitrary

music recommenders

Connectors to social networks & semantic

web sources are added continuously

Open sourced under Apache License 2.0:

dbis.informatik.uni-freiburg.de/MuSe

An public instance of MUSE with a small user

community is online:muse.informatik.uni-freiburg.de

Web-based User Interface

Register & interconnect social identities

Listen & rate recommended songs

Manage new recommenders & evaluations

Data Repositories

3 shared data structures (restricted access!)

MUSIC RETRIEVAL ENGINE collects new songs

and retrieves meta information

USER PROFILE ENGINE retrieves information

from linked social profiles

Recommender Manager

Coordinates interaction of recommenders

with users and the data repositories

Forwardes user request & receives

generated recommendations

Composes list of recommendations

Recommendation Model

A Music Recommendation Management System

MUSE simplifies development and evaluation of music

recommendation algorithms

Provides many tools for developing recommenders

Takes care of typical tasks of in vivo evaluations

Open sourced under Apache License 2.0

Future Work

Increase flexibility of evaluations

Further connectors for social networks

New algortihms for music recommendation

Case Study

48 participants

1567 rated song

recommendations

Overall, 73% of all

songs positively

rated

dbis.informatik.uni-freiburg.de/MuSe

Configure & manage evaluations Browse & analyze results

Compose recommendations

Country ChartsInter-country view on songs

Social Tags & Social NeighborhoodSocial Networks and Semantic Web as source of information

City Charts Explore upcoming city trends

Annual Charts Taste in music evol-ves between the age of 14 and 20 [11]

Administrative access gives control and insign

DevelopersUsers

Compose recommendation lists out of diverse algortihms

MUSE

PLAYING

SONGS

USER MANAGEMENT

EXTENSIBILITY

EVALUATIONSPRIVACY & SECURITY

MUSIC RECOMMENDERS

Listen & rate recommended songs

Quality score per recommender type {love 2, like 1, dislike -1}

Let s stop reinventing the wheel and put the funback in developing great new algorithms!

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