improvise

A User-centeredMusic Recommendation Approach

for Daily Activities

Ricardo Dias, Ricardo Cunha, Manuel J. FonsecaINESC-ID/Instituto Superior Técnico, Universidade de Lisboa

Music listening on daily activities

Mark Stevens@Flickr

Listening experience is ubiquitous

clemsonunivlibrary@flickr

> 20 Million Songs

Hard to choose songs

Orin Zebest@flickr

Different users, Different tastes

Chloe Muro@flickr

How to recommend songs?

Jacob Bøtter@flickr

Neglecting User Control

Improvise

davejdoe@flickr

User-centered approach

mammal@flickr

RELATED WORK

Different Approaches

Demographic

Collaborative Filtering

Content

Context

Hybrid

[Celma][Celma2010]

Collaborative Filtering

Song 1 Song 2 Song 3 Song 4 Song 5

Alice 5 3 4 4 ?

User 1 3 1 2 3 3

User 2 4 3 4 3 5

User 3 3 3 1 5 4

User 4 1 5 5 2 1

Content

Moyan Brenn@flickr

Context

Peter Merholz@flickr

Where are the users?

• Text

Goals

1. Generic recommendation technique that takes user input for personalization

2. Recommend songs to be listened performing different activities

IMPROVISE

Recommend songs for Activities

abethne15@flickr

User-centered

the rik pics@flickr

Personalization

Approach

Song1Song1

Song1Song1

Song1Song1

Song1Song5

Song2Song2

Song2Song3

Activity 1

Activity 2

Activity 3

giuvanpelt@flickr

Content for Similarity

ASSOCIATING SONGS WITH ACTIVITIES

• Text

Web Application

• Text

Selection Flow

15 Genres

30 Artists of the selected genres

100 songs from those artists

• Text

Data Collected

98 participants

251 answers to the activities

8,518 songs associated to the activities

• Text

Song Characterization

Accousticness Energy Loudness Tempo

4D vector of content features

Ourania@flickr

Analyze to detect similarities

One feature, one interval

Tempo

Max

Min

[Min; Max]

Hyper-Rectangles

Running

Relaxing

Shopping

One Hyper-Rectangle, 4 Features

Tempo Accoustiness Energy Loudness

• Text

One to One

One Activity

One Hyper-rectangle

• Text

Main Challenge

Defining the hyper-rectangles

Establishing the min/max values for each one the four intervals

HYPER-RECTANGLES

Nick Wheeler@flickr

How to choose the best method?

• Text

Classification Task

Activity 2

Activity 3

Activity 2

Song3

• Text

Testing Procedure

Two datasets for training classifier: top-100 and the top-20

Hyper-rectangles determined using the different methods

• Text

First Trial

M1 - AVG +/- STD for the min/max of each interval

M2 – 10%/90% percentiles for min/max

Running

Relaxing

Shopping

Intervals overlapped

• Text

Second Trial

M3 - % of the STD (15% to 30%) + AVG

M4 - % of the STD (15% to 30%) + Median

Increments of 5% from 15% to 30%

Top-100 songs dataset

Median +/- 20% of the STD

Best Approach

GENERIC RECOMMENDER

Cold-start Approach

Top-100 songs selected by the 98 users for each activity

4 hyper-rectangles generated using the Median +/- 20% of the STD

Generic Model

PERSONALIZATION

Greg Peverill-Conti@flickr

How about individual tastes?


Method

Same as in Generic Model

Top 100 songs

Median +/- 20% of STD


Add new songs

List of SongsTop

Bottom

New Song


First In, First Out

Top

Bottom

Top

BottomIn In

Out Out

Song

Song

Time


Benefits

1. New songs remain more time in the list to determine the hyper-rectangles

2. Constantly adapts to users current preferences and tastes

EVALUATION

1. Recommend songs to different activities

2. Personalize the recommendations to users with different tastes

Goals

2 F / 8 M

Aged between 22 and 29 years old

Students (graduate and undergraduate from computer science)

Participants

1. Generic model: 10 users

2. Personalized model (two iterations)1. First iteration: 10 users2. Second iteration: 5 users

Two Experiments

PROCEDURE

Characterize users:

•Gender

•Age

• Listening Habits (frequency)

• Etc.

Demographic Questionnaire

Song Selection

Selecting appropriate songs from a list of 50 songs (one list per activity)

Evaluation Task

Satisfaction Questionnaire

Nr. of songs

Metric

RESULTS

Lambros Roussodimos@flickr

Happy users

yourte contemporaine@flickr

Personalized better than Generic

Generic Model

12

38

0 5 10 15 20 25 30 35 40

Selected

NotSelected

Number of Songs

Generic Model

12

38

0 5 10 15 20 25 30 35 40

Selected

NotSelected

Number of Songs

24%

Personalized Model (1º)

12

38

15

35

0 5 10 15 20 25 30 35 40

Selected

NotSelected

Personalized Generic

Personalized Model

12

38

15

35

0 5 10 15 20 25 30 35 40

Selected

NotSelected

Personalized Generic

25%

Personalized Model (2º)

10% increase

Satisfaction Results

Special cases

User 1

User 2

User 3User 4

User 5

Discussion

For some users more interaction is required to improve recommendation

Might improve by using more songs than just the top 100

Number of songs steadily increased

LIMITATIONS

Lack of comparison

No comparison between the

Generic Recommender and other solutions

Elvin@flickr

Few Users

Peter Merholz@flickr

Activities were simulated

CONCLUSIONS AND FUTURE WORK

Recommend songs for Daily Activities

Silvia Viñuales@flickr

User-centered Approach

ca_heckler@flickr

Experimental Evaluation

Kat@flickr

More evaluation required

Explore new methods

Use more songs

> 8.000

A User-centeredMusic Recommendation Approach

for Daily Activities

Ricardo Dias, Phd Student and Researcher

http://web.tecnico.ulisboa.pt/ricardo.dias

The End

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