affective recommender systems: the role of emotions in recommender systems

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  • 1.Affective recommender systems: the role of emotions in recommender systems Marko Tkali, Andrej Koir, Jurij TasiUniverza v Ljubljani..: Fakulteta za elektrotehniko:..[LDOS]..: Laboratorij za digitalno obdelavo signalov, slik in videa:..

2. 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 3. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. Introduction Its about music, not about recommenders (Eric Bieschke, Pandora) Re: Its about us, the users RecSys help us make DECISIONS on content items Bounded rationality theory [Daniel Kahnemann (nobel prize foreconomics 2002)]Decision making = rational + emotional 4. 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% 5. 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, Hanjali, Nunes, Tkali CAMRA 2010 contest Overlapping with the affective computing community 6. 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: Its 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 7. 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 8. 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 9. 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 10. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. Circumplex model Maps basic emotions dimensional modelArousalhigh joyanger surprisedisgust fear Valence neutralnegative positivesadnesslow 11. 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) 12. 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 others work Goal: Researchers to identify their position To benefit from each others work To establish affective recommender system as a (sub)field? References are in the paper 13. Univerza v Ljubljani..: Fakulteta za elektrotehniko:..[LDOS]..: Laboratorij za digitalno obdelavo signalov, slik in videa:..The proposed framework - 1time choice Give GiverecommendationscontentContent applicationEntry stage Consumption stageExit stage 14. Univerza v Ljubljani..: Fakulteta za elektrotehniko:.. [LDOS]..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. The proposed framework - 2 timeEntry moodExit moodchoiceDetectGive Giveentry recommendationscontentmood Content application Context Decision making Influence Diversification Entry stage Consumption stageExit stage 15. Univerza v Ljubljani..: Fakulteta za elektrotehniko:.. [LDOS]..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. The proposed framework - 3 timeEntry moodContent-induced affective statechoiceDetectGive Giveentry Observe user recommendationscontentmood Content application Affective tagging Affective user profiles Entry stage Consumption stageExit stage 16. Univerza v Ljubljani..: Fakulteta za elektrotehniko:.. [LDOS]..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. The proposed framework - 3 timeEntry moodContent-induced affective state Exit moodchoiceDetect DetectGive Giveentry Observe userexit recommendationscontentmood mood Content application Implicit feedback Evaluation metrics Entry stage Consumption stageExit stage 17. Univerza v Ljubljani..: Fakulteta za elektrotehniko:.. [LDOS]..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. The proposed framework - 3 timeEntry moodContent-induced affective state Exit moodchoiceDetect DetectGive Giveentry Observe userexit recommendationscontentmood mood Content application Context Decision making Affective tagging Affective user profiles Implicit feedback Influence Evaluation metrics Diversification Entry stage Consumption stageExit stage 18. 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 19. 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? 20. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..Notes