personality-based recommender systems: an...

Personality-based recommender systems:

an overview

Maria Augusta S. N. Nunes (UFS)- [email protected] Hu (EPLF)[email protected]

1

mardi 11 septembre 12

mailto:[email protected]


Maria Augusta S. N. Nunes

Docteur en Informatique - Université Montpellier II - 2008

Professor at Universidade Federal de Sergipe - Brazil - from 2009

this presentation is being supported by the Brazilian government:

2


partly from Sergipe-FAP:

and Universidade Federal de Sergipe, head of Capacite project:

3


Rong Hu

Docteur en Science -Swiss Federal Institute of Technology, Lausanne(EPFL)- 2012.

Post-doctoral at Swiss Federal Institute of Technology, Lausanne(EPFL)- from 2012

This presentation is being supported by École Polytechnique Fédérale de Lausanne (EPFL)

4


Outline

• First part - basic theories and Knowledge towards to PBRS:

• Contextualization of Affective computing at RecSys;

• Human decision-making & computer decision making;

• Personality theory;

• Personality extraction;

• Personality profile representation and standardization.

• Second part - Personality-based Recommender Systems:

5


RecSys slogan:

6


So what???

Are you sure about this?

....

7


Do you remember ....

How about the old times??

8


9


Imagine:

(even if the pharmacist doesn’t know you, could he offer you something adequate?)

10


just by looking at your physiological situation...

11


Could this information help?

physiological

psycho-affective

12


So...going back to the slogan…

Is recommendation something new?

13


How about computers?

Could computers effectively understand your psycho-affective, physiological data (subtle information)?

Then, could computers offer you something new?

Look at AMAZON, for instance...

14


15


Do computers effectively know you?

16


Does computer knows you?

17


18


How much information do computers know about you?

Do they perceive some subtle information?

19


How about products?

How much subtle information does AMAZON know about its products?

How many products does it have in order to match your expectations?

Are there too much data to analyze and make a good recommendation ???

20


OVERLOAD....YES....21


Now, Affective Computing comes!!

in order to improve recommendations, it might treat 2 aspects:

tailoring user needs;

addressing the cold-start problem;

22


Affective Computing& Human Decision-

Making

23


AC: what is it about?

How to recognize/extract emotions;

how to model emotions;

how to express emotions;

how to simulate/feel emotions (robots);

how induce emotions in humans.

[Picard, 1997]

24


Why use it?

improve the human-machine interface;

optimize/personalize human-computer interaction;

improve the computer decision-making (based on human metaphor);

25


However...

metaphorically, think ...

When you feel some emotion in your life, is it related to some other psychological aspect?

what aspect?

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Why have psychologists been studying so many psychological aspects including emotion and Affective Computing scientists so few?

27


...such as Personality;

Unfortunately, Affective Computing scientists started to study personality much later than emotions;

for instance, Lisetti [2002], describes how important personality is ...

28


[Lisetti, 2002]29


Imagine a shoppingscenario

at a real (physical-offline) shopping center in town:

human decision-making;

at a virtual shop (online)- such as Amazon:

computer decision-making;

How would a vendor personalize and recommend products for you?

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31


Do computers use this type of information?

32


Coming back to the Recsys slogan...

Studies have demonstrated how important psychological aspects of people, such as Personality Traits and Emotions, are during the human decision-making process [Damasio, 1994; Simon, 1983; Picard, 1997; Trapp at al, 2003 and Thagard, 2006].

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Is recommender systems, implemented in computers, something new?

and, if it uses psychological aspects, such as personality, then, is it new?

why couldn’t we implement in computers a metaphor of the human decision-making process in order to improve personalization, investing in returning clients?

34


Emotion & Personality

35


Emotion

instantaneous;

short life-time;

changes constantly;

dependent on events in the environment;

36


Personality

more stable;

in adulthood, remains stable over a 45-year period;

37


Emotions

easy measurable in humans;

physiological information;

intrusive methods;

modeled in computers to improve the user-computer interaction;

38


Personality

hard to extract in a short interaction;

hard to extract from the user intentionally;

personality implies Emotions;

39


Emotion can indicate the user’s psychological state (mood) at a given moment ...

However, it does not give an indication of what kind of product the user might be interested in.

40


Personality Theorydoes not have a common definition:

Funder [2001]:

human thinking patterns +

emotions +

behaviors +

others psychological mechanisms .

41


Many approaches [Funder, 2001]:

trait approach;

biological approach;

psychoanalytic approach;

phenomenological-humanistic approach;

behavioral approach;

and cognitive approach.

42


Trait approach

differentiates people psychologically by using conceptualized and measurable traits ;

Traits is a formal way to implement personality in computers;

more used by computer scientists;

43


Allport 1921 = 17,953 traits [Allport, 1921];

Cattel proposes 4,500 traits;

Later, reduced 99% by orthogonal methods, concluding that only 5 factors were replicable [Goldberg, 1990]:

the formal beginning of the Big Five [John and Strivastava, 1999].

44


The Big Five factors/dimensions

Extraversion;

Agreeableness;

Conscientiousness;

Open to experience;

Neuroticism.

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46


• personality might predict emotion:

• look at:Extraverts:

but «context-aware» [Ricci, 2012];

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Computational Personality Acquisition methods

Explicit methods:

test-based (questionnaire-based);

story-based;

Implicit methods:

text-based;

keyboard-based;

kinect-based;

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Personality: test-based

computer narrative:

set of traits;

differentiates someone from another;

many inventories are based on the Big Five:

49


• 240-items NEO-PI-R (Revised NEO Personality Inventory) [Costa and MCrae,1992];

• 300-items NEO-IPIP (International Personality Item Pool, Neuroticism-Extroversion-Openness Personality Inventory) [Johnson, 2000; Johnson, 2005]. IPIP Consortium [Goldberg, 1999];

• 5 Big Five factors + 30 facets;

• ......

• 10-items TIPI (Ten-Item Personality Inventory) [Gosling et al, 2003];

• 5 Big Five factors;

psychometric quality different from the bigger one;

50


NEO-IPIP and TIPI -Universidade Federal de Sergipe’s Version

Old web version:

51


• new Mobile based version

PersonalityML

52


Personality: story-based

• stories that represent the Big Five traits (polarized model : high & low);

• based on 20 item from IPIP;

• [Dennis et al, 2012] parlty satisfied with the results;

53


• http://homepages.abdn.ac.uk/m.dennis/pages/w/?page_id=231

54


http://homepages.abdn.ac.uk/m.dennis/pages/w/?page_id=231




• We are working on transforming the NEO-IPIP and TIPI into a « story » (such as we did in the «comic book»)

55


Personality: text-based

• Psychologists said that language can be used as a psychological marker [Pennebaker et al, 2002];

• then, Mairesse et al [2007] developed the Personality Recognizer that extracts information from the way people use words (personality cues);

56


• How did Mairesse develop his experiment?

• collects individual corpora;

• extracts relevant features from texts based on LIWC (a Dictionary-based identification, created for the Linguistic Inquiry and Word Count-LIWC- program and Medical Research Council (MRC) Psycholinguistic database dictionary);

• collects associated personality ratings (based on NEO-PI-R- Big Five factors);

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• builds statistical models of personality ratings;

• uses regression algorithms to estimate the scores of Big Five Personality Traits

• http://people.csail.mit.edu/francois/research/personality/demo.html

58


http://people.csail.mit.edu/francois/research/personality/demo.html




• Minamikawa and Yokoyama [2011]:

• create a tool to extract personality from Japanese blogs in order to recommend groups:

• use Multinomial Naïve Bayes;

• use an Egogram (integrative approach from psychology and psychotherapy (psychoanalytic, humanist and cognitive approaches));

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http://en.wikipedia.org/wiki/Psychology

http://en.wikipedia.org/wiki/Psychology

http://en.wikipedia.org/wiki/Psychotherapy

http://en.wikipedia.org/wiki/Psychotherapy

http://en.wikipedia.org/wiki/Psychoanalysis

http://en.wikipedia.org/wiki/Psychoanalysis

http://en.wikipedia.org/wiki/Secular_humanism

http://en.wikipedia.org/wiki/Secular_humanism

http://en.wikipedia.org/wiki/Cognitive

http://en.wikipedia.org/wiki/Cognitive

• Nunes et al at UFS:

• we are doing a portuguese version of Personality Recognizer:

• LIWC, WordNet (not good in portuguese);

• Onto.PT (http://ontopt.dei.uc.pt/)

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http://ontopt.dei.uc.pt/

http://ontopt.dei.uc.pt/

Personality: keyboard-based

• Gosling [2008] said that individuals consciously and unconsciously leave traces of their individuality in the spaces around them;

• Why not by keyboard typing?

• Montalvão and Freire [2006] said that each person has his own typing pattern;

61


• Porto and Costa [2011] developed an experiment to recognize human personality by using typing patterns:

• extract the user’s typing rhythm (KeyPress, «hold time», keyDown, keyUp)

• collect associated personality ratings from NEO-IPIP;

• apply a clustering technique to match the typing rhythm and personality;

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• We did 5 experiments:

• from 282 to 85 participants;

• we find some correlation in 10 facets;

• more results to be published;

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Personality Profile representation and

standardization

64


How do we represent and store the Personality extracted before?

Identitiy from the real world is stored in virtual world as an User profile;

However, can the traditional “Profile” store the psychological aspects, such as personality?

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yes !!! GUMO Ontology- [Heckmann, 2005];

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GUMO (Generic User Model)-2005. Unfortunately, people do not effectively use it;

UPP - User Psychological Profile-2007:

used in Recommender Systems [Nunes, 2008];

how about the standardization?

68


PersonalityML

comes to standardize the representation of Personality;

XML based;

recommender inputs;

...

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70


71


availabe at www.personalityresearch.com.br

Personality ML Structure;

xsd;

“comic book”.

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http://www.personalityresearch.com.br

http://www.personalityresearch.com.br

Personality-based Recommender Systems

Rong HuHCI Group, EPFL

Contact: [email protected]




Outline

• Personality-based recommender technologies and systems

• User perception issues

• Conclusions

• Future research and application directions

74


Motivation

75

Human Behaviors

Human Decision Making

Interests

Personality


• How to use personality in recommender Systems?

• How about user experience?

76


PBRS Technologies

Personality Acquisition User Modeling

Recommendation Generation User Perception

77


PBRS Technologies



78


PBRS Technologies



79


Personality in social matching system

80


PB Social Matching System

81



82


PB Social Matching System• .

83


The psychological literature indicates there is a strong relationship between personality similarity and attraction.

People prefer to interact with others who have similar Personality.

84[Nass and Lee, 2000; Reeves and Nass, 1996 ]



85[Nunes, 2008]


Recommending President• Recommending a “French Presidential candidate”

based on psychological reputation of presidential candidates (December 2006 - July 2007, covering the Elections for President in France)

86Ségolène Royal Nicolas Sarkozy


Recommending President

87


Personality in content-based information filtering systems

88


Temperament-based Filtering

89[Lin and McLeod, 2002]


Temperament-based Filtering1. Segment information

into subspaces based on temperaments

2. To reduce the size of comparisons when searching within a segment, segments are clustered by content-based approach.

3. Infer the target segment and cluster

S1S2 S3

S4 S5

S6

S7 S8

S9

S16

S12 S13

S14 S15

S11

S10

Legend:Information SpaceDSJ DNT

DSPDNF

Figure 2. Segments of a sample information space.

given that temperament type, P(likek|t). In learning the temperament concept to segment the information space, an information unit k of the training data set is not classified into Dt until P(likek|t) exceeds a predefined threshold ! to maintain a confidence level that the system believes in the learned knowledge. Table 1 shows an example of the statistical results obtained for 5 information units in the training data set rated by 1000 simulated users, where “yes” indicates “like” and “no” indicates “dislike”. The popularity of d3 is zero for all the temperament types, thus d3 is classified into S16 = D

! in

which an information unit is not evaluated as “like” by any user. On the other hand, three popularity values of d9 are nonzero: P(liked9|SJ) = (97/(97+370)) ! 0.21, P(liked9|SP) = (14/(14+200)) ! 0.07, and P(liked9|NT) = (51/(51+110)) ! 0.32. Assume that threshold ! = 0.10, then d9 is considered liked by SJs and NTs but not SPs or NFs, because only P(liked9|SJ) and P(liked9|NT) exceed !. Hence, d9 is an element in both DSJ and DNT but not DSP or DNF, and is classified into S7 where S7 = DSJ " DNT – DSP – DNF by definition. The knowledge of the segment concept is represented by a set of ordered pairs (item id, segment #), such as (d3, S16) and (d9, S7).

Item SJ SP NT NF Id Yes No Yes No Yes No Yes No d3 0 467 0 214 0 161 0 158d9 97 370 14 200 51 110 0 158

d15 66 401 50 164 92 69 57 101d23 23 444 57 157 46 115 0 158d36 24 443 59 155 95 66 68 90

Table 1. Example of the statistical results obtained for some information units in the training data set.

2.2.2 Temperament-weighted Centroids To reduce the size of comparisons when searching within a segment, the information units in the same segment are clustered by content-based approach that adopts conventional vector space model. Both information units and user requests are represented as term vectors in an n-dimensional space. Each term weight TWi of a term mi in vector d is computed by TF-IDF (Term Frequency and Inverse Document Frequency) method and is often defined as

! "

"

#

!

n1j

2j

2j

iii

IDFTF

IDFTFTW

where TFi = the number of times term mi appears in information unit d, IDFi = log2(n/DFi) = the inverse document frequency, DFi = the number of information units in the collection which contain mi, and n = the number of information units in the collection. The commonly used cosine similarity of any two vectors Vi and Vj is denoted simply as Sim(Vi , Vj ) = (Vi # Vj) / (|Vi| $ |Vj|). A higher cosine value indicates a greater similarity. The basic idea of clustering is to compare a new item with the centroid vector of a cluster and group it into the cluster if the similarity measure is greater than a fixed threshold ". The centroid vector of a cluster is defined as the mathematical average of the information vectors in the cluster. To keep the number of items neither too large nor too small and without losing representative characteristics in a cluster, " was set to 0.07. In content-based filtering, with a collection of 2,000 information units in the database, around 200 clusters were generated with an average of about 10 units each. The same " value was applied in temperament-based filtering when given the user interest key terms, otherwise " was set to zero when only the segments rather than the clusters were concerned. The temperament weight wj of an information unit dj in segment Sn is set to the fraction of relevant temperaments in Sn multiplied by the the prior probability P(likenj) that an information unit dj in segment Sn is evaluated as “like”. Given the probabilities of the temperaments P(t) in the training data, then wj is just

)()()( tPtlikePTT

likePTT

wnTt

njn

njn

j !""

!

where n, Tn, T, and |T| are as defined previously, |Tn| = size of Tn, P(likenj |t) = the conditional probability that an information unit dj in segment Sn is evaluated as “like”, given user temperament t, and P(t) = the percentage distribution of temperament t. The centroid temperament weight ei of a cluster ci is the mathematical average of the temperament weights of the information units in the

cluster and is given by !"

!

m

1jji w

m1e where m = size of

cluster ci and wj = the temperament weight of an information unit dj in cluster ci. 2.3 Inference in the Classification Agent The assumption underlying temperament-based filtering is that a new information unit having content features similar to that of a widely liked set of information units by a particular group of people is probably liked by that group of people. The classification agent of the temperament-based filtering employs the learned temperament concept to infer the maximum likelihood estimate of the target segment and cluster (Starget , Ctarget)

Segments of a sample information space

90


S1S2 S3

S4 S5

S6

S7 S8

S9

S16

S12 S13

S14 S15

S11

S10


DSPDNF



! in



d15 66 401 50 164 92 69 57 101d23 23 444 57 157 46 115 0 158d36 24 443 59 155 95 66 68 90



! "

"

#

!

n1j

2j

2j

iii

IDFTF

IDFTFTW


)()()( tPtlikePTT

likePTT

wnTt

njn

njn

j !""

!



!

m

1jji w




• More than 85% of the user population would be better satisfied.


91


S1S2 S3

S4 S5

S6

S7 S8

S9

S16

S12 S13

S14 S15

S11

S10


DSPDNF



! in



d15 66 401 50 164 92 69 57 101d23 23 444 57 157 46 115 0 158d36 24 443 59 155 95 66 68 90



! "

"

#

!

n1j

2j

2j

iii

IDFTF

IDFTFTW


)()()( tPtlikePTT

likePTT

wnTt

njn

njn

j !""

!



!

m

1jji w




• More than 85% of the user population would be better satisfied.

• Address the new user problem


91


Personality in collaborative filtering systems

92


Personality-based CF• Rating-based Similarity

• Neighborhood Formation (e.g., Pearson Correlation)

• Rating Prediction

93


Personality-based CF

Is it possible to use human personality characteristics to alleviate the cold-

start problem in CF?

94


Personality-based CF• Personality-based CF

• Personality characteristics are used to calculate the similarity of users

• Linear Hybrid CF

95[Rong and Pu, 2011]


Personality-based CF• Cascade Hybrid CF

user 1 user 2 user 3 user 4

item 1 1 0

item 2 0 1

item 3 0 1

item 4 1 096


Personality-based CF• Cascade Hybrid CF

user 1 user 2 user 3 user 4

item 1 1 1 0 0

item 2 0 0 1 1

item 3 0 0 1 1

item 4 1 1 0 097


Results

0.900

0.950

1.000

1.050

1.100

1.150

1.200

Given 2 Given 5 Given 10

MAE

RB PB RPBL RPBC-5

(RB: rating based CF, PB: personality based CF, RPBL: rating-personality based linear hybrid approach, RPBC-5: rating-personality based cascade hybrid approach with β = 5)

Prediction performances in the scenario of new user

98


Results

(RB: rating based CF, PB: personality based CF, RPBL: rating-personality based linear hybrid approach, RPBC-5: rating-personality based cascade hybrid approach with β = 5)

Prediction performances in the scenario of sparse dataset

0.9

0.95

1

1.05

1.1

1.15

1.2

50% 75% 100%

MAE

RB PB RPBL RPBC-5

99


100

New User Problem Period (CSP)

50

p values of the t-test of the comparison of the personality based USM and rating based USM.

[Tkalčič et al., 2011]mardi 11 septembre 12

TWIN Recommender

101[Roshchina et al., 2011]


TWIN Recommender

102


Personality in Group Recommenders

103


104

Personality Aware Group Recommendations



105

Assertiveness

Cooperativeness

Competing

Collaborating

Avoiding

Accommodating

Compromising


• Assertive behaviours penalize the differences with the best choice of another members

• Cooperative behaviours reward the differences with the best choice of another members.

• Conflict Mode Weight (CMW) = 1 + Assertiveness - Cooperativeness

106[Recio-Garcia et al., 2010]



• 90% of groups with more accurate recommendations have at least a member with a high assertive value. (leader)

• Recommender works better for groups with a high dispersion in the CMW value

107[Recio-Garcia et al., 2010]



Personality-based Recommender Applications

108


• Gift finder (Gift.com)

109


Examples• Gift finder (Gift.com)

110


• Relationship (Parship.ch)

111


Examples• Movie Recommender (Whattorent.com)

112


• More ...

113


PBRS Technologies



114


User Perception

User Satisfaction of Personality-based Recommender Systems

115


Study 1

1. Can personality quiz-based recommendation method be accepted by users?

2. Which aspects of the system would influence user acceptance of personality-based approaches?



Study Setup

117


Study Setup

• Evaluation Criteria: Technology Acceptance Model (TAM) [Davis 1889]

Perceived Usefulness

Perceived Ease of Use

Behavioral Intention to Use- Intention to

purchase- Intention to return- Intention to

introduce to friends

Actual System Use

118


Results• Recommendation Accuracy: not significantly

different

3.47 3.573.3 3.13

1

2

3

4

5

The movies recommended for mematched my interests.

I am not satisfied with the movies thissystem recommended to me.

(reversed response)

Personality quiz-based Rating-based(p = 0.58) (p = 0.21)

Strongly Disagree

Strongly Agree

119


Results• Perceived Ease of Use

• Actual Task Completion Time

• Whattorent: 6.8m vs. MovieLens: 18.7m (p < 0.001)

3.833.37

2.3 2.43

1

2

3

4

5

I found it easy to give my initialpreferences.

It required too much effort to ratemovies/ answer personality quiz.

(reversed response)

Personality quiz-based Rating-based

(p < 0.001) (p < 0.001)

Strongly Disagree

Strongly Agree

120


Results• Intention to Use

• Preference: 53% Whattorent vs. 13% MovieLens

3.47 3.333.10

3.432.67

2.47

1

2

3

4

5

Intention to purchase Intention to return Intention to introduce thissystem to friends

Personality quiz-based Rating-based

(p = 0.91) (p < 0.05)(p = 0.052)

Strongly Disagree

Strongly Agree

121


Results• Correlation Analysis

•

122


Study Conclusion

• Ease of use is one dominant merit of the personality-based approach

• Perceived accuracy and ease of use determine users’ acceptance of the personality-based system

• More subjects preferred the personality-based system.

• Problem: Transparency

123


Study 2

• Investigate the feasibility of using personality quizzes to build user profiles not only for an active user but also his or her friends (i.e., for self vs. for friends)

• Investigate the influence of domain knowledge on user perception of personality-based recommender systems (i.e., domain experts vs. domain novices)



Study Setup• Personality Evaluation

• TIPI (Ten Item Personality Inventory) [Gosling et al., 2003]

• Participants

• 80 subjects (32 females) from 17 countries

• expert users (17), medium users (32), novice users (23)

• User Tasks

• User personality quiz to find songs for self and one friend

125


Study Setup• Evaluation Criteria: ResQue Model [Pu, Chen and

Hu, 2011]

Qualities of Recommend

ed Items

Interaction Adequacy

Interface Adequacy

Perceived Ease of Use

Perceived Usefulness

Control/Transparenc

y

Satisfaction

Trust

Confidence

Use the System

Purchase

Continuance

Social Influence

System Quality Beliefs Attitudes Behavioral

Intentions

126


Results• Self- vs. Friend-Recommendations

Average users’ responses to the subjective measurements (1: strongly disagree, 5:strongly agree)

2.9 2.8 3.13.6

4.3

3.53.1

2.4

3.2 3.13.1 3.1 2.9 3.2

4.3

3.52.9

2.53.0 3.1

1

2

3

4

5

perceivedeffectiveness

perceivedaccuracy(p<0.05)

perceivedhelpfulness

enjoyment(p<0.05)

ease of use satisfaction use intention purchaseintention

returnintention(p<0.05)

referenceintention

Find Songs For Self Find Songs For Friends

127


Results

• Medium Users vs. Expert Users

• Novice Users vs. Expert Users

128


Results

• Medium Users vs. Expert Users

• Novice Users vs. Expert Users

Subjective responses in the scenario of finding songs for self (Expert uses: 17, Medium users: 32, Novice users: 23)

128

2.3 2.4 2.3

3.5

4.6

3.22.4 2.0

2.7 2.63.3 3.0 3.3 3.6

4.33.5 3.3

2.73.3 3.2

2.7 2.83.3 3.6

4.03.5 3.2

2.43.3 3.0

1

2

3

4

5

perceivedeffectiveness

(p<0.05)

perceivedaccuracy(p<0.05)

perceivedhelpfulness

(p<0.05)

enjoyment ease of use satisfaction use intention(p<0.05)

purchaseintention

returnintention

referenceintention

expert users medium users novice users


Study Conclusion

• Users with low level of music domain knowledge gave higher subjective evaluation scores than domain experts

• There is a system-adaptivity requirement

• Problem: Privacy and Control

129


Conclusions

• Advantages

• Provide personalized services

• Enhance the interaction experience between systems and users

• Address the cold-start problem

• ...

130


Conclusions

• Disadvantages

• Transparency, privacy and control Issues

• Difficult to acquire users’ personality

• It is not intuitive to have the relations between personality characteristics and recommended items

• ...

131


Future Directions

• Design efficient and pleasant ways to acquire users’ personality information

• Develop methods which automatically mapping personality characteristics and items or item features

• Design friendly user interfaces for PBRS

• Need a lot of work...

132


Conclusions

• Why scientists stopped publishing in the personality field (I mean, this year)?

• is personality hard to extract ?

• is personality hard to formalize and store ?

• Is it already standardized ? (to be used anywhere as recommender inputs, cookies)?

133


New research directions

at Universidade Federal de Sergipe:

Geolocated personality-based recommender systems for Brazilian mega events 2014-2016 (Personal-Movie);

134


Group recommender 3.0 - for mobile;

135


PersonalityML 2.0

136


Personality Recognizer:

by « comic book » stories;

by text in Portuguese (text-mining);

by Typing;

by Kinect;

137


Project with Univ. Montpellier II -Lirmm- France:

treating Post-Stroke patients by using Affective computing in order to recommend the better rehabilitation, considering patient motivation;

138


www.personalityresearch.com.br

http://200.17.141.213/~gutanunes/

[email protected]

139


http://www.portalpersonality.com.br

http://www.portalpersonality.com.br

http://200.17.141.213/~gutanunes/

http://200.17.141.213/~gutanunes/



Thank you very much!

questions?

140


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classification and measurement. Journal of Abnormal and Social Psychology, (16):6–40, 1921.

• P. T. Costa and R. R. Mccrae. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI): Professional manual, 1992.

• A. R. Damasio. Descartes’ Error: Emotion, Reason, and the Human Brain. Quill, New York, 1994.

• F. D. Davis. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3):319–340, 1989.

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