Affect- and Personality-based Recommender Systems Part I: - PowerPoint PPT Presentation

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! Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 2/64

Netflix Netflix . . . one of the greatest players in recommender systems! What is Netflix recommending us? Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 2/64

Netflix Netflix . . . one of the greatest players in recommender systems! What is Netflix recommending us? Movies/films . . . really? Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 2/64

Netflix Netflix . . . one of the greatest players in recommender systems! What is Netflix recommending us? Movies/films . . . really? “I want to watch a funny movie tonight” Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 2/64

Netflix Netflix . . . one of the greatest players in recommender systems! What is Netflix recommending us? Movies/films . . . really? “I want to watch a funny movie tonight” Funny is all you want? Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 2/64

But there’s more!! Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 3/64

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 Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 3/64

Table of Contents Introduction Why are theory-driven models important? Models of Emotion and Personality Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 4/64

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 . Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 5/64

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 Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 6/64

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 Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 7/64

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 We will learn • Part I (Tuesday, 16:30 - 18:30) • why models borrowed from psychology and social sciences are important • models (emotions, personality) Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 7/64

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 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 Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 7/64

Table of Contents Introduction Why are theory-driven models important? Models of Emotion and Personality Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 8/64

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) Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 9/64

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) Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 9/64

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) Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 9/64

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) • 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. Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 9/64

Decision making Liking, purchasing, rating, clicking etc. . . actions triggered by decisions Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 10/64

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. Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 10/64

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 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. 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. Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 11/64

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 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. Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 12/64

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 Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 13/64

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. Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 14/64

Kahneman Tversky two systems Marko Tkalčič, RecSys2017SummerSchool-Part1-AffectRecSys 15/64