D5.4 Discovery algorithms and models for social

PHENICX-WD-WP1-UPF-130315- DeliverableTemplate-1.2 Page 1 of 34 PHENICX_WD_WP1_DeliverableTemplate_20120314_MTG-UPF Page 1 of 34

D5.4 Discovery algorithms and models for social communities: Algorithms for inferring community structures and models of typical digital social communities

Grant Agreement nr 601166

Project title Performances as Highly Enriched aNd Interactive Concert

eXperiences

Project acronym PHENICX

Start date of project

(dur.)

Feb 1st, 2013 (3 years)

Document reference PHENICX-D-WP5-JKU-130731-

D5.4_DiscoveryAlgorithmsAndModelsForSocialCommunities

-03

Report availability PU - Public

Document due Date July 31th, 2014

Actual date of delivery July 31th, 2014

Leader JKU

Reply to Marko Tkalčič ([email protected])

Additional main

contributors (author’s

name / partner acr.)

Markus Schedl (JKU)

Cynthia Liem (TUD)

Mark Melenhorst (TUD)

Document status Final

PHENICX-WD-WP1-UPF-140520-DeliverableTemplate -1.2 Page 2 of 34

Project funded by ICT-7th Framework Program from the European Commission

Table of Contents

EXECUTIVE SUMMARY ......................................................................................................................... 3

1 BACKGROUND ..................................................................................................................................... 5

2 INTRODUCTION .................................................................................................................................. 6

2.1 MAIN OBJECTIVES AND GOALS .......................................................................................... 6

3 DATA ACQUISITION .......................................................................................................................... 6

4 USER CLUSTERING PROCEDURE .................................................................................................. 8

5 CLUSTERING USERS BASED ON DEMOGRAPHICS ................................................................. 8

6 CLUSTERING BASED ON MUSIC PREFERENCES .................................................................... 12

7 CLUSTERING BASED ON PREFERENCES OF SUPPORTING MULTIMEDIA MATERIAL ............................................................................................................................................... 18

8 CONCLUSION ..................................................................................................................................... 22

9 REFERENCES ...................................................................................................................................... 23

9.1 WRITTEN REFERENCES ....................................................................................................... 23

9.2 WEB REFERENCES ................................................................................................................. 23

9.3 TRADEMARKS AND COPYRIGHTS .................................................................................... 23

9.4 ACRONYMS AND ABBREVIATIONS.................................................................................. 23

10 APPENDIX ..................................................................................................................................... 24

10.1 LIST OF AUTHORS ................................................................................................................. 24

10.2 APPENDIX 2: USER STUDY SURVEY PAGES ................................................................. 25


Executive Summary

This deliverable captures the work done on the segmentation of users within the WP5 work package (Profiling and personalisation). More specifically, the deliverable reflects the work done

in task T5.4 (Matching users at different levels of specificity).

The task T5.4 takes as input the data from task T5.1 (Mining user-related profile information),

where D5.2 (Standardized corpus of user profiling information) is reported, and provides part of

the inputs for the task T6.2 (Personalized multimodal information system).

This deliverable presents methods for the discovery of social communities, i.e. how user group

together based on specific properties. The clustering of users is very important for the personalization part of the project, as it will provide the basis for the development of the

personalization algorithms.

The identification of user clusters in the feature space designed for this deliverable is of paramount importance for the design and development of the personalized multi-modal

information system.

One of the demonstrators of the personalized system will be the Use Case 1 “Digital Program

Notes”. In this demonstrator, a user will get a personalized set of supporting material (in forms of additional text, images or sounds about the performer, piece or composer). In order to develop

user modeling techniques for this kind of personalization a suitable dataset is required. As D5.1

and D5.2 showed, the consumption logs on mobile devices and the huge social media streams do not contain enough useful information to perform a satisfactory modeling of users for the

specific case of the personalized multimodal information system as foreseen in the DoW.

Hence, we carried out a user study to collect the needed features and user preferences. Based

on these data we performed user-clustering techniques to identify in which aspects users can be

grouped.

Bearing in mind, that the personalization will be included in the use case “Digital Program Notes”

where a collection of personalized supporting multimedia material will be delivered to the user based on his personal profile, we carried out a user study on Amazon Mechanical Turk. The

subjects were asked three set of questions: (i) demographics (age, gender, music listening behaviours, personality), (ii) general music preferences (genre preferences) and (iii) preferences

about supporting multimedia material. Tha latter was composed of a set of 14 conditions where

users were asked to rate different aspects of supporting multimedia items of different lengths(long/short), modalities (text, images, audio) and entities (composer, piece, performer).

In order to achieve the grouping of users as requested in the DoW we performed user clustering in three types of features: demographics, general music preferences, preferences about the

supporting multimedia material.

The following steps were carried out for each type of features:

Step 1: Principal component Analysis (PCA) to identify the variables that account for most

of the variance. This step was done for two reasons (i) dimensionality reduction and (ii)

identification of the variables with the highest variance, which are the candidates for clustering

Step 2: visual inspection of the distribution of data points (single- and pair-wise) In this

step we were inspecting visually the distributions of the data points within a single feature


(histograms) and pair-wise. This inspection was important to visually identify the

clustering of data points as well as possible correlations between variables

Step 3: GMM clustering on the most variable features (pair-wise) In this last step we

performed the Gaussian Mixture Model (GMM) clustering with two clusters over pairs of

features

For the demographics features, data points did not show any natural pair-wise clusters, except

for the personality factors of extraversion and agreeableness where there appear to be two distinct clusters: (i) users with high extraversion and low agreeableness and (ii) users with low

extraversion and high agreeableness.

In the general music preferences set of features some of the genre features (avant-garde, heavy metal and rap) exhibit bimodal histograms, hence suggesting clustering of users into those that

like the genre and those who don’t like the genre. When observing the genre features pairwise and perform the clustering we observed that some genre pairs tend to produce clusters (e.g. the

latin-reggae, the international-latin, the electronic-new age or the country-religious pairs).

In the feature space of preferences about the supporting multimedia material the distributions of answers to the questions for each condition follow a bell-shaped curve. Due to the large number

of possible combinations the pairwise analysis has not been concluded yet as of the writing of this deliverable.

This deliverable will be used by tasks T6.2 (Personalized multimodal information system), tasks

T7.1 and T7.2 (especially the demonstrator UC1: Digital Program Notes).

The following steps need to be taken in order to reach fully functional user models for the

personalized system:

Perform a more in-depth cluster analysis of clustering for the supporting MM material

group of features

Apply cluster quality measures (e.g. between-variance-within-variance ratio)

Perform regression analysis to develop predictive models from independent variables

(which can be acquired through initial questionnaires, past usage or cross-domain) to dependant variables (preferences about the supporting multimedia material)


1 BACKGROUND

This deliverable captures the work done on the segmentation of users within the WP5 work

package (Profiling and personalisation). More specifically, the deliverable reflects the work done in task T5.4 (Matching users at different levels of specificity).

The task T5.4 takes as input the data from task T5.1 (Mining user-related profile information),

where D5.2 (Standardized corpus of user profiling information) is reported, and provides part of the inputs for the task T6.2 (Personalized multimodal information system).

This deliverable presents methods for the discovery of social communities, i.e. how user group together based on specific properties. The clustering of users is very important for the

personalization part of the project, as it will provide the basis for the development of the

personalization algorithms.

According to the description of work, this deliverable requires to address:

Relating users to each other based on differing profile attributes, such as demographics,

taste

Relating users to each other through user’s relation to musical items, which can consider

topics such as familiarity with a musical item, and general interaction information

The expected result is a multi-faceted and flexible addressing of user-user and user-item-user similarity.

As reported in the deliverable D5.1 social media streams contain little information about classical

music usage. Also, as reported in D5.2, mobile users rarely consume classical music. Hence, in addition to the corpora reported in these two deliverables, we collected additional data in order

to gain insights into users preferences and consequently be able to devise methods for the discovery and modeling of social communities.

Furthermore, the personalization process will be shown in the demonstrator “Digital Program

Notes” (UC1).

In this deliverable we report the acquisition of the required data through a user study and the

analysis of the clustering of users on this dataset.


2 INTRODUCTION

2.1 Main objectives and goals

The main objective of this deliverable is to report on the data and methods employed to perform user-clustering techniques. The identification of user clusters in the feature space designed for

this deliverable is of paramount importance for the design and development of the personalized multi-modal information system.

One of the demonstrators of the personalized system will be the Use Case 1 “Digital Program Notes”. In this demonstrator, a user will get a personalized set of supporting material (in forms

of additional text, images or sounds about the performer, piece or composer). In order to develop

user modeling techniques for this kind of personalization a suitable dataset is required. As D5.1 and D5.2 showed, the consumption logs on mobile devices and the huge social media streams

do not contain enough useful information to perform a satisfactory modeling of users for the specific case of the personalized multimodal information system as foreseen in the DoW.

Hence, we carried out a user study to collect the needed features and user preferences. Based

on these data we performed user-clustering techniques to identify in which aspects users can be grouped.

In this document we first present the acquisition of the data required (Sec. 3). Then we outline

the clustering technique involved (Sec. 4). Finally, we present the outcomes of the clustering

techniques in Sects. 5, 6 and 7.

3 DATA ACQUISITION

When designing the data acquisition we had in mind the use case “Digital Program Notes” where

a collection of personalized supporting multimedia material will be delivered to the user based on

his personal profile. Hence, a user study has been conducted with 167 participants, mostly from the US. The subjects were put in a set of conditions that reflected combinations of three variables

with the following values (lengths):

- Textual information (long/short)

- Audio information (single/multiple) - Pictures (single/many)

The variables were related to the following entities:

- Composer - Orchestra

- Piece In total we had 14 conditions:


condition

ID length modality entity

1 long text composer

2 short text composer

3 short image composer

4 long image composer

5 long audio composer

6 short text piece

7 long text piece

8 short image piece

9 long image piece

10 short text orchestra

11 long text orchestra

12 short image orchestra

13 long image orchestra

18 long audio piece

Furthermore we collected features that we hypothesized could be good predictors of the users'

clusters. We collected two more groups of data: (i) demographics and (ii) music preferences.

In the demographics section, we asked the users to provide us with the following information:

- Age - Gender

- Time spent listening to classical music - Time spent listening to non-classical music

- Time spent playing an instrument

- Musical education - Number of attended classical concets

- Number of attended non-classical concerts

Furthermore, we asked the subjects to fill in the Ten-Items-Personality questionnaire [tipi] to

assess their personality profiles in terms of the Five Factor Personality Model (FFM) [Gosling et al. 2003].

In the music preferences section we asked the subjects to express their preferences towards

some music genres. The set of genres was selected from the most popular genres at the Last.fm

website.

The user-study web survey is available here: http://bird.cp.jku.at/phenicx_us_guest/ [survey] Screenshots are in Appendix 10.2.

http://bird.cp.jku.at/phenicx_us_guest/


4 USER CLUSTERING PROCEDURE

In order to achieve the grouping of users as requested in the DoW we performed user clustering

in three types of features: i. Demographics

ii. General music preferences

iii. Preferences about the supporting multimedia material

For each type of features, we followed the clustering method outlined in Figure 1.

Figure 1: The clustering method employed

The following steps were carried out for each type of features:

Step 1: Principal component Analysis (PCA) to identify the variables that account for

most of the variance. This step was done for two reasons (i) dimensionality reduction and (ii) identification of

the variables with the highest variance, which are the candidates for clustering Step 2: visual inspection of the distribution of data points (single- and pair-wise)

In this step we were inspecting visually the distributions of the data points within a single

feature (histograms) and pair-wise. This inspection was important to visually identify the clustering of data points as well as possible correlations between variables

Step 3: GMM clustering on the most variable features (pair-wise) In this last step we performed the Gaussian Mixture Model (GMM) clustering with two

clusters over pairs of features

5 CLUSTERING USERS BASED ON DEMOGRAPHICS

The PCA analysis showed that 95 % of the variance is explained by the first four components, as

can be seen in Figure 2.

User features

Principal Component

Analysis

Selected features

Histogram visual

inspection

Pairwise GMM

clustering

GMM clusters models


Figure 2: PCA analysis of the demographics features

The first two components account for roughly 80% of the variance. The feature weights in the first two components are the following (sorted by the absolute value of the weights):

First component:

feature ID feature name weight

10 Listening-non-classical 0.927461

7 Age -0.357710

14 concert-non-clasical 0.101956

11 playing 0.032736

12 musical education 0.016817

9 Listening-classical 0.006512

8 Gender 0.004539

1 Extraversion -0.004093

2 Agreeableness -0.003092

13 concert-clasical -0.002865

4 Stability 0.002661

3 Conscientiousness 0.002059

5 Openness 0.001450

1 2 3 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Principal Component

Variance E

xpla

ined (

%)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%


Second component:

feature ID feature name weight

7 Age 0.931621

10 Listening-non-classical 0.360512

12 musical education 0.029036

11 playing -0.026504

9 Listening-classical 0.017113

13 concert-clasical -0.011386

14 concert-non-clasical -0.008378

5 Openness -0.005510

8 Gender -0.003827

2 Agreeableness 0.003812

4 Stability 0.003533

1 Extraversion -0.001385

3 Conscientiousness 0.000743

Based on the above tables we selected those features that are present in the first two factors and have the absolute value of the weight higher than 0.1. These features are : 7 (age), 10

(listening non-classical), 14 (concert non-classical).

Furthermore, a visual inspection and the GMM clustering technique in the space of the first two

principal components suggest that users do not tend to group in clusters, as can be seen in

Figure 3.

Figure 3: GMM clustering on the first two PCA components of the demographics features

-40 -20 0 20 40 60 80 100-30

-20

-10

0

10

20

30

40

first PCA component

second P

CA

com

ponent

GMM clusters of the first two PCA components


The visual inspection of the histograms of the observed features (Figure 4) suggest that the data

do not form clusters.

Figure 4: Histograms of the demographics features

A visual inspection of the pairwise plot of data points suggested that the data do not group in

clusters except for the features extraversion and agreeableness (Figure 5).

0 1 20

50

100Extraversion

0 1 20

50Agreeableness

0 0.5 1 1.50

50

100Conscientiousness

0 1 20

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100Stability

0 1 20

50Openness

20 40 60 800

50Age

0 0.5 10

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200Listening-classical

5 15 253545 5565 7585950

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200Listening-non-classical

0 10 20 300

100

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0 20 400

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200musical education

0 5 100

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200concert-clasical

0 20 40 600

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Figure 5: GMM clusters in the agreeableness and extraversion features space

On our sample there appear to be two distinct clusters: users with high extraversion and low agreeableness and users with low extraversion and high agreeableness.

6 CLUSTERING BASED ON MUSIC PREFERENCES

The features used in the clustering based on music preferences are the features describing the

user preferences to various genres of music. The genres that define the features are: Avant-Garde

Blues

Classical

Country

Easy Listening

Electronic

Folk

Heavy Metal

International

Jazz

Latin

New Age

Pop/Rock

R&B

Rap

Reggae

-0.5 0 0.5 1 1.5 2

0

0.5

1

1.5

2

2.5

Extraversion

Agre

eable

ness

pdf(obj,[x,y])


Religious

Vocal

As can be seen in Figure 6, the variance is more equally distributed over several components.

Figure 6: PCA of the features describing user genre preferences

PCA first component:

25 latin 0.308804

23 international 0.303600

24 jazz 0.289231

30 reggae 0.283428

16 blues 0.283175

28 rnb 0.279361

15 avant garde 0.264402

32 vocal 0.259802

29 rap 0.250575

18 country 0.247058

21 folk 0.243983

26 new age 0.205037

31 religious 0.202804

19 easy listening 0.186288

20 electronic 0.166741

17 classical 0.135895

27 pop rock 0.073136

22 heavy metal -0.000545

1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Principal Component

Variance E

xpla

ined (

%)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%


PCA second component:

22 heavy metal 0.521260

18 country -0.391733

19 easy listening -0.375172

31 religious -0.366743

30 reggae 0.274788

20 electronic 0.274458

29 rap 0.229055

32 vocal -0.200794

16 blues 0.124734

15 avant garde 0.114366

23 international 0.099874

25 latin -0.078235

28 rnb 0.064448

24 jazz 0.054952

27 pop rock -0.046903

21 folk 0.039674

17 classical -0.012432

26 new age 0.011101

Distribution and GMM clustering of the first two PCA components reveals a tendency to cluster

into two groups, although very close, as shown in Figure 7.

Figure 7: Clustering in the space of the first two PCA components of the genre preferences space

-12 -10 -8 -6 -4 -2 0 2 4 6 8-5

-4

-3

-2

-1

0

1

2

3

4

5

first PCA component

second P

CA

com

ponent



The histograms are presented in Figure 8, the distributions of preferences for genres have

different patterns. While most of the genres exhibit a somewhat bell-shaped distribution, some

of the genres appear exhibit a bimodal histogram, implying that users tend to cluster into those who like the genre and those who don’t like the genre (e.g. avant-garde, heavy metal or rap).

Figure 8: Distribution of genre preferences among users

Some combinations of genre features tend to create clusters of users as can be seen in Figure

9 (latin-reggae), Figure 10 (international-latin), Figure 11 (electronic-new age) or Figure 12

(country-religious).

0 50

50

100avant garde

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100blues

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100classical

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50country

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100easy listening

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Figure 9: Clusters in the latin-reggae feature space

Figure 10: Clusters in the international-latin feature space

-1 0 1 2 3 4 5 6-1

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latin

reggae

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international

latin


Figure 11: clusters in the elctronic-new age feature space

Figure 12: Clusters in the country-religious feature space

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electronic

new

age

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ious


7 CLUSTERING BASED ON PREFERENCES OF SUPPORTING MULTIMEDIA MATERIAL

In terms of supporting multimedia material, we want to discover how users form clusters based

on two aspects of the supporting MM item: likeness and novelty. In fact, for each of the items in the user study, we asked the users two questions:

1. »I find the content above interesting« to measure how much the user liked the item 2. »I learned something new from the content above.« to measure if the item was novel to

the user

Also for each of the items we also asked the users how much of the items did she/he consume. Hence, for each item (condition in the user study) we had therefore three features: the

consumption, the likeness and the novelty. Given 14 conditions, we had a total of 42 features.

The PCA revealed that the first component explains much more variance (more than 30%) than

the other components (each less than 10 %). The threshold of 95% of the whole variance is reached with the first ten components (out of 42 variables).

The coding of the feature names was done by merging the condition ID and the question type. For each condition, the user was asked three questions. Beside the two questions mentioned

above we also asked how much of the item the user has consumed. The conditoin ID and the question are separated by a hyphen. Hence the variable name »c5-A2« means »condition 5,

answer 2«. The coding scheme is outlined in Table 1.

1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Principal Component

Variance E

xpla

ined (

%)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%


condition

ID length modality entity

consumption question 1

(likeness)

question 2

(novelty)

1 long text composer c1-consumption c1-A1 c1-A2

2 short text composer c2-consumption c2-A1 c2-A2

3 short image composer c3-consumption c3-A1 c3-A2

4 long image composer c4-consumption c4-A1 c4-A2

5 long audio composer c5-consumption c5-A1 c5-A2

6 short text piece c6-consumption c6-A1 c6-A2

7 long text piece c7-consumption c7-A1 c7-A2

8 short image piece c8-consumption c8-A1 c8-A2

9 long image piece c9-consumption c9-A1 c9-A2

10 short text orchestra c10-consumption c10-A1 c10-A2

11 long text orchestra c11-consumption c11-A1 c11-A2

12 short image orchestra c12-consumption c12-A1 c12-A2

13 long image orchestra c13-consumption c13-A1 c13-A2

18 long audio piece c18-consumption c18-A1 c18-A2

Table 1: Feature names coding table

The first PCA component is composed as follows:

34 c1-A1 0.236708

33 c1-consumption 0.230158

51 c7-A1 0.217614

46 c5-A1 0.203067

37 c2-A1 0.202655

63 c11-A1 0.200896

73 c18-A2 0.195757

60 c10-A1 0.193796

64 c11-A2 0.193683


35 c1-A2 0.192121

45 c5-A1 0.188850

52 c7-A2 0.184513

48 c6-A1 0.179007


40 c3-A1 0.172805

67 c12-A2 0.166570

41 c3-A2 0.165165

72 c18-A1 0.158511

38 c2-A2 0.152254



55 c8-A2 0.130973

58 c9-A2 0.128763

43 c4-A2 0.128714

61 c10-A2 0.128081



49 c6-A2 0.120396

57 c9-A1 0.118104





70 c13-A2 0.103594

54 c8-A1 0.103187


66 c12-A1 0.098989




69 c13-A1 0.082330

The first two components do not appear to form distinct clusters

The distributions of answers to the questions for each condition follow a bell-shaped curve as can

be seen in Figure 13.

-12 -10 -8 -6 -4 -2 0 2 4 6 8-6

-4

-2

0

2

4

6

first PCA component

second P

CA

com

ponent



Figure 13: Histograms of the 42 features on preferences of the supporting multimodal material

Due to the large number of possible combinations, the pairwise analysis has not been concluded

yet as of the writing of this deliverable.

0 50

50100

c1-consumption

0 50

50100

c1-A1

0 50

50100

c1-A2

0 50

100200

c2-consumption

0 50

50100

c2-A1

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50100

c2-A2

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50100

c3-consumption

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50100

c3-A1

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c3-A2

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c4-consumption

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c4-A2

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c5-consumption

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c5-A1

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c5-A1

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c6-consumption

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c6-A1

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c6-A2

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50100

c7-consumption

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50100

c7-A1

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50100

c7-A2

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50100

c8-consumption

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50100

c8-A1

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c8-A2

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50100

c9-consumption

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50100

c9-A1

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c9-A2

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c10-consumption

0 50

50100

c10-A1

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c10-A2

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c11-consumption

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50100

c11-A1

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c11-A2

0 50

50100

c12-consumption

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100200

c12-A1

0 50

50100

c12-A2

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c13-consumption

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50100

c13-A1

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c13-A2

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50100

c18-consumption

0 50

50100

c18-A1

0 50

50100

c18-A2


8 CONCLUSION

In this deliverable, we presented the outcomes of the data collection and clustering of users

based on demographics, genre preferences and preferences about the supporting multimedia materials.

The analysis of user clusters, done using GMM, showed that users generally do not tend to form distinctive clusters, except for certain personality traits (extraversion-agreeableness) and genre

preferences (e.g. country-religious).

This deliverable will be used by tasks T6.2 (Personalized multimodal information system), tasks

T7.1 and T7.2 (especially the demonstrator UC1: Digital Program Notes).

The following steps need to be taken in order to reach fully functional user models for the personalized system:

Perform a more in-depth cluster analysis of clustering for the supporting MM material

group of features Apply cluster quality measures (e.g. between-variance-within-variance ratio)

Perform regression analysis to develop predictive models from independent variables

(which can be acquired through initial questionnaires, past usage or cross-domain) to

dependant variables (preferences about the supporting multimedia material)


9 REFERENCES

9.1 Written references

Hu, R., & Pu, P. (2010). Using Personality Information in Collaborative Filtering for New Users. Recommender Systems and the Social Web, 17. Retrieved from

http://www.dcs.warwick.ac.uk/~ssanand/RSWeb_files/Proceedings_RSWEB-10.pdf#page=23 Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender

Systems. Computer, 42(8), 30–37. doi:10.1109/MC.2009.263 Adomavicius, G., & Tuzhilin, a. (2005). Toward the next generation of recommender systems:

a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749. doi:10.1109/TKDE.2005.99

Tkalcic, M., Kunaver, M., Košir, A., & Tasic, J. (2011). Addressing the new user problem with

a personality based user similarity measure. Joint Proceedings of the Workshop on Decision Making and Recommendation Acceptance Issues in Recommender Systems (DEMRA 2011) and the 2nd Workshop on User Models for Motivational Systems: The Affective and the Rational Routes to Persuasion (UMMS 2011). Retrieved from http://ceur-ws.org/Vol-740/DEMRA_UMMS_2011_proceedings.pdf#page=106

Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in Personality, 37(6), 504–528. doi:10.1016/S0092-

6566(03)00046-1

9.2 Web references

[tipi] http://homepage.psy.utexas.edu/HomePage/Faculty/Gosling/tipi%20site/tipi.htm

[survey] http://bird.cp.jku.at/phenicx_us_guest/

9.3 Trademarks and copyrights

9.4 Acronyms and abbreviations

http://www.dcs.warwick.ac.uk/~ssanand/RSWeb_files/Proceedings_RSWEB-10.pdf#page=23

http://ceur-ws.org/Vol-740/DEMRA_UMMS_2011_proceedings.pdf#page=106

http://ceur-ws.org/Vol-740/DEMRA_UMMS_2011_proceedings.pdf#page=106

http://homepage.psy.utexas.edu/HomePage/Faculty/Gosling/tipi%20site/tipi.htm

http://bird.cp.jku.at/phenicx_us_guest/


10 APPENDIX

10.1 List of authors

Complete this section with the full list of authors that have contributed to the deliverable, including

also their affiliation.

Marko Tkalčič JKU

Markus Schedl JKU

Cynthia Liem TUD

Mark Melenhorst TUD


10.2 Appendix 2: User study survey pages










Documents

D5.4 Discovery algorithms and models for social