Affect- and Personality-based Recommender Systems Part I: Motivation, Models

Aﬀect- and Personality-based Recommender Systems
Part I: Motivation, Models
Marko Tkalčič, Free University of Bozen-Bolzano
ACM Summer School on Recommender Systems 2017
Marko Tkalčič, RecSys2017SummerSchool-Part1-AﬀectRecSys 1/64

Netﬂix
Netﬂix . . . one of the greatest players in recommender
systems!

Netﬂix
systems!
What is Netﬂix recommending us?

Netﬂix
systems!
Movies/ﬁlms . . . really?

Netﬂix
systems!
“I want to watch a funny movie tonight”

Netﬂix
systems!
“I want to watch a funny movie tonight”
Funny is all you want?

But there’s more!!

But there’s more!!
Question:
Can (rating/genre/year/director)
summarize that rollercoaster?
Thanks to Shlomo Berkovski for the inspiring example from the EMPIRE 2015 keynote.
Image source: http://yhvh.name/?w=2646

Table of Contents
Introduction
Why are theory-driven models important?
Models of Emotion and Personality

Who am I?
Marko Tkalčič
• 2016 - now : assistant professor at Free University of
Bozen-Bolzano
• 2013 - 2015: postdoc at Johannes Kepler University,
Linz
• 2011 - 2012: postdoc at University of Ljubljana
• 2008 - 2010: PhD student at University of Ljubljana
My research explores ways in which psychologically-motivated user characteristics,
such as emotions and personality, can be used to improve recommender systems
(personalized systems in general). It employs methods such as user studies and
machine learning.

Book, 2016
• Tkalčič, M., Carolis, B. De, Gemmis, M. de, Odić, A., &
Košir, A. (Eds.). (2016). Emotions and Personality in
Personalized Services. Springer International Publishing.
https://doi.org/10.1007/978-3-319-31413-6
• Authors from
• Stanford, Cambridge, Imperial College, UCL . . .
• topics:
• psychological models
• acquisition of emotions/personality
• personalization techniques
• http://www.springer.com/gp/book/9783319314112

Goal of this Talk/Learning Outcomes
• complements other talks of the Summer School
• oﬀ the beaten track . . . meant to open new ideas
• part of the audience should say
• this is BS
• this is inspiring

• this is BS
We will learn
• Part I (Tuesday, 16:30 - 18:30)
• why models borrowed from psychology and social sciences are important
• models (emotions, personality)

• this is BS
We will learn
• Part I (Tuesday, 16:30 - 18:30)
• why models borrowed from psychology and social sciences are important
• models (emotions, personality)
• Part II (Thursday, 8:15 - 10:15)
• tools for acquiring E&P
• usage of E&P in recommender systems

Table of Contents
Introduction

What are we predicting in RS?
. . . for each user, we want to choose such item that maximizes the user’s utility/rating.
(Adomavicius, Tuzhilin, 2005)

Recommender Systems (RSs) are software tools and techniques that provide suggestions
for items that are most likely of interest to a particular user (Ricci et al., 2015)

Recommender Systems (RSs) are software tools and techniques that provide suggestions
for items that are most likely of interest to a particular user (Ricci et al., 2015)
• what inﬂuences (which features)?
• how (which algorithm)?
References
Adomavicius, G., and 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.
Ricci, F., Rokach, L., and Shapira, B. (2015). Recommender Systems: Introduction and Challenges. In Recommender Systems
Handbook (Vol. 54, pp. 1–34). Boston, MA: Springer US.

Decision making
Liking, purchasing, rating, clicking etc. . . actions triggered by decisions

Decision making
Liking, purchasing, rating, clicking etc. . . actions triggered by decisions
One way of looking at recommender systems is the one taken by (Jameson et al., 2015):
we view recommender systems as tools for helping people to make better choices
—not large, complex choices, such as where to build a new airport, but the small- to
medium-sized choices that people make every day: what products to buy, what
documents to read, which people to contact.
References
Jameson, A., Willemsen, M. C., Felfernig, A., de Gemmis, M., Lops, P., Semeraro, G., and Chen, L. (2015). Human Decision Making
and Recommender Systems. In Recommender Systems Handbook (Vol. 54, pp. 611–648). Boston, MA: Springer US.

Decision making models
There are many decision-making models
• ASPECT/ARCADE (Jameson et al.)
• Somatic Markers (Damasio)
• Two Systems (Kahneman and Tversky)
• Multi-system model (Lerner et al.)
References
Damasio, A. (1994) Descartes’ Error: Emotion, Reason, and the Human Brain
Kahneman, D. (2003). A perspective on judgment and choice: mapping bounded rationality. The American Psychologist, 58(9),
697–720.
Lerner, J. S., Li, Y., Valdesolo, P., and Kassam, K. S. (2015). Emotion and Decision Making. Annual Review of Psychology, 66(1),
799–823.

ASPECT/ARCADE models
• ASPECT model of choice-making: based on human-choice patterns
• ARCADE model: strategies to support decision making - i.e. by RS
References

Decision making and emotions - Damasio
• physiological/evolutionary aspect
• emotional processes guide (or bias) behavior, particularly decision-making
• changes in both body and brain states in response to diﬀerent stimuli
• these physiological signals (or somatic markers) and their evoked emotion are
consciously or unconsciously associated with their past outcomes and bias
decision-making
References
Damasio, A. (1994) Descartes’ Error: Emotion, Reason, and the Human Brain

Kahneman Tversky two systems
• Decision:
• System 1: fast, intuitive, emotion-driven
• system 2: slow, rational
References
Kahneman, D. (2003). A perspective on judgment and choice: mapping bounded rationality. The American Psychologist, 58(9),
697–720.

Kahneman Tversky two systems

Decision making model - Lerner
emotions and personality inﬂuence our decisions!
References
Lerner, J. S., Li, Y., Valdesolo, P., and Kassam, K. S. (2015). Emotion and Decision Making. Annual Review of Psychology, 66(1),
799–823.

Personality and preferences
Personality traits (extraverted/introverted, open/conservative etc.) are linked to music
genre preferences (Rentfrow et al, 2003)
References
Rentfrow, P. J., and Gosling, S. D. (2003). The do re mi’s of everyday life: The structure and personality correlates of music
preferences. Journal of Personality and Social Psychology, 84(6), 1236–1256.
Tkalčič, M., Ferwerda, B., Hauger, D., and Schedl, M. (2015). Personality Correlates for Digital Concert Program Notes. In UMAP
2015, Lecture Notes On Computer Science 9146 (Vol. 9146, pp. 364–369).

Emotions are related to other things as well
Why we choose to consume some kind of content?

Emotions are related to other things as well
Why we choose to consume some kind of content?
One of the main reasons why people consume music (Lonsdale, 2011) and films (Oliver,
2008) is emotion regulation.
References
Lonsdale, A. J., and North, A. C. (2011). Why do we listen to music? A uses and gratifications analysis. British Journal of Psychology
(London, England : 1953), 102(1), 108–34. https://doi.org/10.1348/000712610X506831
Oliver, M. B. (2008). Tender affective states as predictors of entertainment preference. Journal of Communication, 58(1), 40–61.
https://doi.org/10.1111/j.1460-2466.2007.00373.x

However, it can get complicated
Why do we like drama, sad films?
• . . . under some circumstances, individuals may choose to view entertainment for
reasons that may not be best described as driven by hedonic motivations but
rather as driven by eudaimonic motivations: greater insight, self reflection, or
contemplations of poignancy or meaningfulness (e.g., what makes life valuable).
• The Hangover
• hedonic quality (comedy)
• no eudaimonic quality
• La vita e’ bella
• hedonic quality (comedy)
• eudaimonic quality
References
Oliver, M. B. (2008). Tender affective states as predictors of entertainment preference. Journal of Communication, 58(1), 40–61.
https://doi.org/10.1111/j.1460-2466.2007.00373.x

What we have today?
Features
• genre
• actors (performers, authors, directors
. . . )
• rating
• timestamp
• location
• price
• year
Algorithms
• Content-based
• Collaborative Filtering
• Knowledge-based
• Hybrid

Are these the right features?

Light Darkness
genre emotions
action personality
timestamp
actors
rating
location

Thesis: besides using existing data/features and the respective data-driven models,
theory-driven features and models should be investigated to improve recommender
systems.
Let’s look at a couple of sparse examples illustrating the above thesis.

Chomsky-Skinner Debate
Behaviorism
During the ﬁrst half of the twentieth century, John B. Watson devised methodological
behaviorism, which rejected introspective methods and sought to understand
behavior by only measuring observable behaviors and events (Wikipedia).

Chomsky-Skinner Debate
Behaviorism
During the ﬁrst half of the twentieth century, John B. Watson devised methodological
behaviorism, which rejected introspective methods and sought to understand
behavior by only measuring observable behaviors and events (Wikipedia).
On language acquisition
• Skinner (behaviorist):
• operant conditioning : child goes through trial-and-error she tries and fails to use correct
language until she succeeds; with reinforcement and shaping provided by the parents
gestures (smiles, attention and approval) which are pleasant to the child.
• there’s no need to understand the underlying hardware
• Chomsky (structuralist):
• operant conditioning could not account for a child’s ability to create or understand an
inﬁnite variety of novel sentences
• language acquisition has an innate structure, it is a function of the human brain.
• there are structures of the brain that control the interpretation and production of speech
• it is important to understand the underlying hardware
References
Andresen, J. (1991). Skinner and Chomsky 30 years later. Or: The return of the repressed . The Behavior Analyst, 14(1), 49–60.

Claudia Perlich, keynote @Recsys 2016
• Q: In the world of deep learning, XGBoost etc. why logistic regression?
• A: I studied neural networks in 1995 . . . downgraded to decision trees in 2004
and won 3 KDD cups with linear models. Personally I ﬁnd it easier to build up a
model with interesting feature construction. . . I can look under the hood and see
what these things are doing.
https://www.youtube.com/watch?v=1WmqqfXNFZ4&feature=youtu.be&t=3003

Gary Marcus (NYU): let’s put “top down” and “bottom up” knowledge on
equal footing
To get computers to think like humans, we need a new A.I. paradigm, one that places
top down and bottom up knowledge on equal footing. Bottom-up knowledge is the
kind of raw information we get directly from our senses, like patterns of light falling on
our retina. Top-down knowledge comprises cognitive models of the world and how it
works.
References
Gary Marcus, Artiﬁcial Intelligence Is Stuck. Here’s How to Move It Forward. New York Times, July 29, 2017

Come again?
Why, again, models-driven approaches complementing data-driven?
• models from other disciplines can be inspiring
• such models can account for information variance
• Marvin Minsky created the ﬁrst neural net borrowing the concept from neurology
https://en.wikipedia.org/wiki/History_of_artiﬁcial_intelligence

But, how can we relate this to recommender systems?
• the gap between existing RS and psychological models is too wide
• psychological models of DM can be
• too generic
• too complex
• hard to implement

But, how can we relate this to recommender systems?
• the gap between existing RS and psychological models is too wide
• psychological models of DM can be
• too generic
• too complex
• hard to implement
• let’s make smaller steps
• measure new features and use our (recsys) algorithms
• emotions
• personality
• let’s shed new light

Table of Contents
Introduction

Personality, Mood and Emotions

Personality
• What is personality?
• accounts for individual diﬀerences ( = explains the variance in users) in our enduring
emotional, interpersonal, experiential, attitudinal, and motivational styles

Models of Personality - 4 temperaments
• Four temperaments Hippocratess (cca 400 BC)
• sanguine (pleasure-seeking and sociable)
• choleric (ambitious and leader-like)
• melancholic (analytical and quiet)
• phlegmatic (relaxed and peaceful)

Models of Personality - Big5
• The ﬁve factor model (FFM) – Big5:
• Extraversion
• Agreeableness
• Conscientousness
• Neuroticism
• Openness (to new experiences)
The inverse of Neuroticism is sometimes referred to as Emotional Stability,
References
McCraMcCrae, R. R., and John, O. P. (1992). An Introduction to the Five-Factor Model and its Applications. Journal of Personality,
60(2), p175 – 215.

Individual Factors
• Openness to Experience (O),
• high: imaginative, creative people (individualistic, non conforming and are very aware of
their feelings)
• low: down-to-earth, conventional people. (simple and straightforward thinking over
complex, ambiguous and subtle)
• sub-factors are imagination, artistic interest, emotionality, adventurousness, intellect and
liberalism.
• Conscientiousness
• high : prudent, organized
• low: impulsive, disorganized.
• sub-factors are self-eﬃcacy, orderliness, dutifulness, achievement-striving, self-discipline
and cautiousness.
• Extraversion
• high: degree of engagement with the external world, react with enthusiasm and often
have positive emotions
• low: lack of engagement with the external world, quiet, low-key and disengaged in social
interactions.
• sub-factors of E are friendliness, gregariousness, assertiveness, activity level,
excitement-seeking and cheerfulness.

Individual Factors
• Agreeableness
• high : need for cooperation and social harmony
• low : opposite
• subfactors: trust, morality, altruism, cooperation, modesty and sympathy.
• Neuroticism
• high: emotionally reactive. They tend to respond emotionally to relatively neutral stimuli.
They are often in a bad mood, which strongly aﬀects their thinking and decision making
• low: calm, emotionally stable and free from persistent bad mood.
• sub-factors are anxiety, anger, depression, self-consciousness, immoderation and
vulnerability.

Models of personality - others
• RIASEC occupational model:
• Realistic (Doers),
• Investigative (Thinkers),
• Artistic (Creators),
• Social (Helpers),
• Enterprising (Persuaders)
• Conventional (Organizers)
• Bartle gamers model:
• Killers: interfere with the functioning
of the game world or the play
experience of other players
• Achievers: accumulate status tokens by
beating the rules-based challenges of
the game world
• Explorers: discover the systems
governing the operation of the game
world
• Socializers: form relationships with
other players by telling stories within
the game world
• Myers-Briggs Personality Type
• Extraversion (E) or Introversion (I).
• Sensing (S) or Intuition (N).
• Thinking (T) or Feeling (F).
• Judging (J) or Perceiving (P).
• Learning styles (Felder and Silverman
Learning Style Model):
• active/reﬂective,
• sensing/intuitive,
• visual/verbal,
• sequential/global
• Thomas-Kilmann Conﬂict Mode
• Assertiveness
• Cooperativeness
There are correlations between these models.

Thomas-Kilmann Conflict Mode
• was developed to measure the conflict resolution styles in groups
COMPETING COLLABORATING
COMPROMISING
AVOIDING ACCOMODATING
COOPERATIVENESS
ASSERTIVENESS
HIGH
HIGH
LOW
LOW
References
Thomas, K. L., and Kilman, R. H. (n.d.). Thomas-Kilman Conflict Mode Instrument.

Domain-speciﬁc personality models - tourism
• questionnaire -> factor analysis
• tourist speciﬁc personality constructs
• Sun and Chill-out
• Knowledge and Travel
• Independence and History
• Culture and Indulgence
• Social and Sport
• Action and Fun
• Nature and Recreation
• picture-based measuring instruments
• pick and rank 10 images out of a pool
References
Neidhardt, J., Seyfang, L., Schuster, R., and Werthner, H. (2015). A picture-based approach to recommender systems. Information
Technology and Tourism, 15(1), 49–69. https://doi.org/10.1007/s40558-014-0017-5

Measuring the FFM
• Extensive questionnaires (from 5 to several 100s questions)
• BFI: 44 questions
• TIPI : 10 questions
• NEO-IPIP: 300 questions
• For each user u a ﬁve tuple b = (b1, b2, b3, b4, b5)

TIPI - Ten-Items Personality Inventory
TIPI: I see myself as (1-7 . . .
agree/disagree):
1. Extraverted, enthusiastic.
2. Critical, quarrelsome.
3. Dependable, self-disciplined.
4. Anxious, easily upset.
5. Open to new experiences,
complex.
6. Reserved, quiet.
7. Sympathetic, warm.
8. Disorganized, careless.
9. Calm, emotionally stable.
10. Conventional, uncreative.
• b1 = q1 + (8 − q6) = Extraversion
• b2 = q2 + (8 − q7) = Agreeableness
• b3 = q3 + (8 − q8) = Conscientiousness
• b4 = q4 + (8 − q9) = Emotional Stability
• b5 = q5 + (8 − q10) = Openness to Experiences
References
Gosling, S. D., Rentfrow, P. J., and 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

Let’s measure ourselves
https://goo.gl/forms/qN64cjEaMMlor2F42
Age
group Extraversion Agreeableness Conscientiousness Emotional
Stability Openness
15
to
20 Mean 3.79 4.47 4.41 4.61 5.43
SD 1.55 1.22 1.39 1.47 1.17
n
=
54973 54973 54973 54973 54973
21
to
30 Mean 3.73 4.5 4.57 4.64 5.49
SD 1.54 1.2 1.39 1.46 1.13
n
=
40737 40737 40737 40737 40737
31
to
40 Mean 3.81 4.55 4.77 4.63 5.49
SD 1.54 1.21 1.35 1.42 1.12
n
=
14752 14752 14752 14752 14752
41
to
50 Mean 3.85 4.7 4.96 4.72 5.41
SD 1.54 1.18 1.35 1.39 1.17
n
=
7668 7668 7668 7668 7668
51
to
60 Mean 3.87 4.89 5.11 4.8 5.39
SD 1.54 1.18 1.31 1.38 1.2
n
=
3532 3532 3532 3532 3532
61
and
older Mean 3.85 4.95 5.26 4.92 5.37
SD 1.49 1.17 1.3 1.34 1.26
n
=
905 905 905 905 905
Male
http://gosling.psy.utexas.edu/scales-weve-developed/ten-item-personality-measure-Marko Tkalčič, RecSys2017SummerSchool-Part1-AﬀectRecSys 45/64

Emotion vs. Mood vs. Sentiment
Let’s clear some terminology
• Aﬀect : umbrella term for describing the topics of emotion, feelings, and moods
• Emotion:
• brief in duration
• consist of a coordinated set of responses (verbal, physiological, behavioral, and neural
mechanisms)
• triggered
• Mood:
• last longer
• less intense than emotions
• no trigger
• Sentiment:
• towards an object
• positive/negative

Models of Emotions
• Emotions are complex human experiences
• Evolutionary based
• Several deﬁnitions, we take with simple models, easy to incorporate in computers:
• Basic emotions
• Dimensional model
• Plutchik wheel
• Geneva Emotion Wheel

Basic Emotions
• Discrete classes model
• Diﬀerent sets
• Darwin: Expression of emotions in man and animal
• Ekman deﬁnition (6 + neutral):
• Happiness
• Anger
• Fear
• Sadness
• Disgust
• Surprise

Dimensional model of Emotions
Three continuous dimensions
• Valence/Pleasure (positive-negative)
Each emotion is a point in the VAD space
Self-Assessment Manikin (SAM)

• Arousal (high-low )

• Dominance (high-low )

• Dominance (high-low )
References
Bradley, M. M., and Lang, P. J. (1994). Measuring emotion: the self-assessment manikin and the semantic diﬀerential. Journal of
Behavior Therapy and Experimental Psychiatry, 25(1), 49–59.

Circumplex model of Emotions
mapping between basic emotions and dimensional model
References
Posner, J., Russell, J. a, and Peterson, B. S. (2005). The circumplex model of aﬀect: an integrative approach to aﬀective neuroscience,
cognitive development, and psychopathology. Development and Psychopathology, 17(3), 715–734.
https://doi.org/10.1017/S0954579405050340

Plutchik Wheel of emotions
• Robert Plutchik
• eight basic emotions: Joy, Trust, Fear,
Surprise, Sadness, Disgust, Anger and
Anticipation.
• intensity (radius)
• twenty-four Primary, Secondary, and
Tertiary dyads (a feeling composed of
two emotions)
• Primary dyad = one petal apart =
Love = Joy + Trust
• Secondary dyad = two petals apart =
Envy = Sadness + Anger
• Tertiary dyad = three petals apart =
Shame = Fear + Disgust
• Opposite emotions = four petals apart
= Anticipation != Surprise
References
Plutchik, R. (1982). A psychoevolutionary theory of emotions. Social Science Information, 21(4–5), 529–553.
https://doi.org/10.1177/053901882021004003

Geneva Emotion Wheel
• model
• instrument
for
measuring
emotions
References
Sacharin, V., Schlegel, K., and Scherer, K. R. (2012). Geneva Emotion Wheel Rating Study.

Music-specific emotion model - GEMS
• domain-specific emotion model
• Geneva Emotional Music Scale (GEMS)
• 9-factorial model of music-induced emotions
• transcendence
• wonder
• joyful activation
• power
• tension
• sadness
• tenderness
• nostalgia
• peacefulness
References
Zentner, M., Grandjean, D., and Scherer, K. R. (2008). Emotions evoked by the sound of music: characterization, classification, and
measurement. Emotion (Washington, D.C.), 8(4), 494–521. https://doi.org/10.1037/1528-3542.8.4.494

Modeling content with emotions
• user-related emotions
• how does (recommended) content relate to emotions?
• “I want to watch a funny movie tonight”

Modeling movies with emotions
• “A ﬁlm is - or should be - more like music than like ﬁction. It should be a
progression of moods and feelings. The theme, what’s behind the emotion, the
meaning, all that comes later.” – Stanley Kubrick

• “If my ﬁlms make one more person miserable, I’ll feel I have done my job.” –
Woody Allen

• “If my ﬁlms make one more person miserable, I’ll feel I have done my job.” –
Woody Allen
• “Through careful manipulation and good storytelling, you can get everybody to
clap at the same time, to laugh at the same time, and to be afraid at the same
time.” – Steven Spielberg

Kurt Vonnegut story arc

Modeling movies with emotions - screenwriting workshop
Source:
https://iedgameresearch.wordpress.com/2014/04/30/noel-mccauley-workshop-staging-emotional-environments/

feature-ﬁlm-length emotion maps
http://yhvh.name/?w=-97&emap=1

Modeling music with emotion
(Juslin et al.) distinguish between three types of music-related emotions:
• expressed emotions
• the composer or performer wants to express
• perceived
• how a listener perceives (but not necessarily feels)
• induced
• truly felt by the listener
References
Juslin, P. N., and Laukka, P. (2004). Expression, Perception, and Induction of Musical Emotions: A Review and a Questionnaire Study
of Everyday Listening. Journal of New Music Research, 33(3), 217–238. https://doi.org/10.1080/0929821042000317813

Music emotion detection from movements
• movements stimulated by music
• captured by mobile phone (raw
acceleration data)
• extract motion features
• linear regressoin predicting GEMS
• R2 between 0.14 and 0.50
References
Irrgang, M., and Egermann, H. (2016). From motion to emotion: Accelerometer data predict subjective experience of music. PLoS
ONE, 11(7), 1–20. https://doi.org/10.1371/journal.pone.0154360

Music emotion recognition (MER)
• perceived emotion = a single point in VA space
• annotations
• features:
• acoustic (loudness, timbre, pitch, rhythm,melody, harmony)
• lyrics
• models:
• regression techniques
• probabilistic (take into account the variance)
Dancing Queen
(Abba)
Civil war (GNR) Suzanne (Leonard
Cohen)
All I have to do is
dream (Everly
Brothers)
References
Wang, J.-C., Yang, Y., and Wang, H. (2016). Aﬀective Music Information Retrieval. In M. Tkalčič, B. De Carolis, M. de Gemmis, A.
Odić, and A. Košir (Eds.), Emotions and Personality in Personalized Services: Models, Evaluation and Applications (pp. 227–261).
Springer.Marko Tkalčič, RecSys2017SummerSchool-Part1-AﬀectRecSys 61/64

Wrap-up
• Motivation: emotions and personality should be investigated

Wrap-up
• Models of personality and emotions

Wrap-up
• Models of personality and emotions
• Measurement with self-reporting

Next
• unobtrusive measurement
• usage in recommender systems

Questions?

Affect- and Personality-based Recommender Systems Part I: Motivation, Models

More Related Content

Similar to Affect- and Personality-based Recommender Systems Part I: Motivation, Models (20)

More from Marko Tkalčič (7)

Recently uploaded (20)

Affect- and Personality-based Recommender Systems Part I: Motivation, Models