Affective recommender systems: the role of emotions in recommender systems
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Affective recommender systems: the role of emotions in recommender systems

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Affective recommender systems: the role of emotions in recommender systems Affective recommender systems: the role of emotions in recommender systems Presentation Transcript

  • 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:..
  • 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 high joy anger surprise disgust fear Valence neutral negative positive sadness low
  • 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 time choice Give Give recommendations content Content applicationEntry stage Consumption stage Exit stage
  • Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. The proposed framework - 2 time Entry mood Exit mood choice Detect Give Give entry recommendations content mood Content application• Context• Decision making• Influence• Diversification Entry stage Consumption stage Exit stage
  • Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. The proposed framework - 3 timeEntry mood Content-induced affective state choice Detect Give Give entry Observe user recommendations content mood Content application • Affective tagging • Affective user profiles Entry stage Consumption stage Exit stage
  • Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. The proposed framework - 3 timeEntry mood Content-induced affective state Exit mood choice Detect Detect Give Give entry Observe user exit recommendations content mood mood Content application • Implicit feedback • Evaluation metrics Entry stage Consumption stage Exit stage
  • Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. The proposed framework - 3 time Entry mood Content-induced affective state Exit mood choice Detect Detect Give Give entry Observe user exit recommendations content mood mood Content application• Context• Decision making • Affective tagging • Affective user profiles • Implicit feedback• Influence • Evaluation metrics• Diversification Entry stage Consumption stage Exit stage
  • 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