Affective recommender systems: the role of emotions in recommender systems

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


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


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%


 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


 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


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


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
 Three dimensions
– Valence
– Arousal
– Dominance

 Each emotive state is a point in the VAD space


Circumplex model
 Maps basic emotions dimensional model
Arousal
high

joy
anger
surprise

disgust

fear
Valence
neutral
negative positive
sadness

low


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)


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


The proposed framework - 1
time

choice

Give Give
recommendations content

Content application

Entry stage Consumption stage Exit stage


time

Entry mood Exit mood

choice

Detect
Give Give
entry
mood

Content application
• Context
• Decision making
• Influence
• Diversification


time

Entry mood Content-induced affective state

choice

Detect
Give Give
entry Observe user
mood

Content application
• Affective tagging
• Affective user profiles



time

Entry mood Content-induced affective state Exit mood

choice

Detect Detect
Give Give
entry Observe user exit
mood mood

Content application

• Implicit feedback
• Evaluation metrics



time

Entry mood Content-induced affective state Exit mood

choice

Detect Detect
Give Give
entry Observe user exit
mood mood

Content application
• Context
• Decision making • Affective tagging
• Affective user profiles • Implicit feedback
• Influence • Evaluation metrics
• Diversification


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


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?


Notes

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