affective recommender systems: the role of emotions in recommender systems
TRANSCRIPT
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
Affective recommender systems: the role of emotions in recommender systems
Marko Tkalčič, Andrej Košir, Jurij Tasič
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
Presentation overview Introduction From data-centric to user-centric Overview of emotions Proposed framework Conclusions
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
Introduction It‘s about music, not about recommenders (Eric Bieschke,
Pandora)– Re: It‘s about us, the users
RecSys help us make DECISIONS on content items Bounded rationality theory [Daniel Kahnemann (nobel
prize for economics 2002)]
Decision making = rational + emotional
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
From data-centric to user-centric Early RecSys:
– ratingPredictions(data-centric descriptors)
= descriptors that are available (e.g. from IMDB)
» Genre» Actors» Performers» Timestamps
– Typical modeling:
User ui likes the genre gj under the ck circumstances XX%
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
From data-centric to user-centric In recent years
– shift towards user-centric descriptors
= descriptors that are suspected to carry information
but are NOT available » Emotional responses» Personality
Arapakis, Gonzalez, Hanjalić, Nunes, Tkalčič CAMRA 2010 contest Overlapping with the affective computing community
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
From data-centric to user-centric The data-centric approach is still rooted in the research
community:– It‘s about music, not about recommenders
The community is problem-solving oriented– The existing datasets are real, why building synthetic ones?
Solving existing problems is only a part of research ...
... the other part is generating new knowledge (on how the world works) ...
... which in turn generates new problems ...
... which in turn opens new publishing possibilities
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
Overview of emotions Emotions are complex human experiences Strong physiological background Evolutionary based Several definitions We take with simple models, easy to incorporate in
computers:– Basic emotions– Dimensional model– Circumplex model
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
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
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
Dimensional model Three dimensions
– Valence– Arousal– Dominance
Each emotive state is a point in the VAD space
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
Circumplex model Maps basic emotions dimensional model
Arousal
Valence
high
negative positive
low
neutral
sadness
fear
disgust
surprise
joyanger
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
How to detect emotions? Explicit vs. Implicit Explicit
– Questionnaires (SAM) Implicit:
– Work done in the affective computing community– Different modalities (sources):
• Facial actions (video)• Physiological signals ( GSR, EEG)• Voice• Posture• ...
– ML techniques• Classification (basic emotions)• Regression (dimensional model)
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
The proposed framework Problem statement:
– Research is done in a scattered fashion– Researchers do not benefit from each other‘s work
Goal:– Researchers to identify their position– To benefit from each other‘s work– To establish affective recommender system as a (sub)field?
References are in the paper
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
The proposed framework - 1
Content application
Give conten
t
time
Entry stage Consumption stage Exit stage
Give recommendati
ons
choice
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
The proposed framework - 2
Content application
Entry mood
Detect entrymood
Give conten
t
Exit mood
time
Entry stage Consumption stage Exit stage
Give recommendati
ons
choice
• Context• Decision making• Influence• Diversification
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
The proposed framework - 3
Content application
Entry mood
Detect entrymood
Give conten
t
Content-induced affective state
Observe user
time
Entry stage Consumption stage Exit stage
Give recommendati
ons
choice
• Affective tagging• Affective user profiles
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
The proposed framework - 3
Content application
Entry mood
Detect entrymood
Give conten
t
Content-induced affective state Exit mood
Observe user
time
Entry stage Consumption stage Exit stage
Give recommendati
ons
choice
Detect exit
mood
• Implicit feedback• Evaluation metrics
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
The proposed framework - 3
Content application
Entry mood
Detect entrymood
Give conten
t
Content-induced affective state Exit mood
Observe user
time
Entry stage Consumption stage Exit stage
Give recommendati
ons
choice
Detect exit
mood
• Implicit feedback• Evaluation metrics
• Affective tagging• Affective user profiles
• Context• Decision making• Influence• Diversification
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
Conclusions Research is shifting towards the use of emotions in recsys Emotions have shown to improve recommenders‘
performance Research is sparse and not self-aware The proposed framework should put things in place
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
Questions Q1: does the framework reflect your view of emotions and
recsys? Q2: did we miss something? Q3: emotions related to diversity, user-centric evaluation? Q4: any other issue?
Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
Notes