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4 Workshop on Emotions and Personality in Personalized 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE) 2016 Boston, MA, USA, September 16th, 2016 Proceedings edited by Marko Tkalčič Berardina de Carolis Marco de Gemmis Andrej Košir in conjunction with 10th ACM Conference on Recommender Systems (RecSys 2016) ii Preface This volume contains the papers presented at the 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE), hold as part of the 10th ACM Conference on Recommender System (RecSys), in Boston, MA, USA. RecSys is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user’s preferences. The EMPIRE workshop focuses on the usage of psychologically-based constructs, such as emotions and personality, in delivering personalized content. The 9 (5 long, 4 short) technical papers included in the proceedings were selected through a rigorous reviewing process, where each paper was reviewed by three PC members. The EMPIRE chairs would like to thank the RecSys workshop chairs, Elizabeth Daly and Dietmar Jannach, for their guidance during the workshop organization. We also wish to thank all authors and all workshops participants for fruitful discussions, the members of the program committee and the external reviewers. All of them secured the workshop’s high quality standards. August 2016 Marko Tkalčič Berardina de Carolis Marco de Gemmis Andrej Košir iii iv EMPIRE 2016 Workshop Organization Chairs: Marko Tkalčič, Free University of Bozen-Bolzano, Italy Berardina De Carolis, University of Bari Aldo Moro, Italy Marco de Gemmis, University of Bari Aldo Moro, Italy Andrej Košir, University of Ljubljana, Slovenia Proceedings Chair: Marco de Gemmis, University of Bari Aldo Moro, Italy Web Chair: Marko Tkalčič, Free University of Bozen-Bolzano, Italy Program Committee: Ioannis Arapakis, Eurecat Matthias Braunhofer, Free University of Bozen-Bolzano Iván Cantador, Universidad Autónoma de Madrid Fabio Celli, University of Trento Li Chen, Hong Kong Baptist University Matt Dennis, University of Aberdeen Mehdi Elahi, Polytechnic University of Milan Bruce Ferwerda, Johannes Kepler University Sabine Graf, Athabasca University Peter Knees, Johannes Kepler University Fang-Fei Kuo, University of Washington Neal Lathia, University of Cambridge Matija Marolt, University of Ljubljana Fedelucio Narducci, University of Bari Aldo Moro Giuseppe Palestra, University of Bari Aldo Moro Viviana Patti, University of Turin Matevž Pesek, University of Ljubljana, Slovenia Marco Polignano, University of Bari Aldo Moro Olga C. Santos, aDeNu Research Group (UNED) Björn Schuller, University of Passau / Imperial College London Giovanni Semeraro, University of Bari Aldo Moro Man-Kwan Shan, National Chengchi University Yi-Hsuan Yang, Academia Sinica Martijn Willemsen, Eindhoven University of Technology Yong Zheng, DePaul University External Reviewer: Muhammad Anwar v vi Table of Contents Long Papers Adapt to Emotional Reactions In Context-aware Personalization 1-8 Yong Zheng Investigating the Role of Personality Traits and Influence Strategies on the Persuasive Effect of Personalized Recommendations 9-17 Sofia Gkika, Marianna Skiada, George Lekakos, Panos Kourouthanassis Personality in Computational Advertising: A Benchmark 18-25 Giorgio Roffo and Alessandro Vinciarelli Emotion Elicitation in Socially Intelligent Services: the Intelligent Typing Tutor Study Case 26-33 Andrej Košir, Marko Meža, Janja Košir, Matija Svetina, Gregor Strle Eliciting Emotions in Design of Games – a Theory Driven Approach 34-42 Alessandro Canossa, Jeremy Badler, Magy Seif El-Nasr, Eric Anderson Short Papers The Influence of Users’ Personality Traits on Satisfaction and Attractiveness of Diversified Recommendation Lists 43-47 Bruce Ferwerda, Mark Graus, Andreu Vall, Marko Tkalčič, Markus Schedl A Jungian based framework for Artificial Personality Synthesis 48-54 David Mascarenas A Comparative Analysis of Personality-Based Music Recommender Systems 55-59 Melissa Onori, Alessandro Micarelli, Giuseppe Sansonetti Recommender System Incorporating User Personality Profile through Analysis of Written Reviews 60-66 Peter Potash, Anna Rumshisky vii viii Adapt to Emotional Reactions In Context-aware Personalization Yong Zheng Department of Information Technology and Management School of Applied Technology Illinois Institute of Technology Chicago, Illinois, USA [email protected] ABSTRACT attributes as the observed contexts which may change when the user Context-aware recommender systems (CARS) have been devel- performs the same activity repeatedly [38]. For example, the time oped to adapt to users’ preferences in different contextual situa- and location may change every time when a user tries to watch a tions. Users’ emotions have been demonstrated as one of effective movie. The season or trip type may change when a user is going to context information in recommender systems. However, there are reserve a hotel. In addition to these factors, users’ emotional states no work exploring the effect of emotional reactions (or expressions) are one of these dynamic variables. And emotions may change in the recommendation process. In this paper, we assume that users anytime in the process of user interactions with the items or the may give similar ratings even if they present different emotional applications. These emotional information have been demonstrated reactions or expressions on the movies. We further model the traits as effective and influential context in previous research [45, 44]. of emotional reactions and incorporate them into context-aware Emotional reactions or expressions are highly correlated with matrix factorization as regularization terms. Our experimental the traits of user personalities. Personality accounts for the most results based on the LDOS-CoMoDa movie data set validate our important ways in which individuals differ in their enduring emo- assumptions and prove that it is useful to take emotional reactions tional, interpersonal, experimental, attitudinal and motivational into consideration in context-aware recommendations. styles [24]. In the domain of recommender systems, personality can be viewed as a user profile, which may be context-independent and domain-independent. Both emotional information [18, 11, Keywords 34, 45] and user personality [31, 19, 35] have been successfully context, recommendation, emotion, emotional reactions incorporated into recommender systems by existing research. Our previous research [45] has successfully utilized emotional variables as contexts in recommender systems to improve recom- Categories and Subject Descriptors mendation performance. Unfortunately, as far as we know, there H.3.3 [Information Search and Retrieval]: Information filtering, are no research on exploring the effect of emotional reactions Retrieval models; H.1.2 [Models and Principles]: User/Machine or expressions. We believe that users’ emotional reactions or Systems - human information processing expressions are also useful to model users’ preferences or rating behaviors in real practice. For example, two different users may 1. INTRODUCTION AND BACKGROUND give high ratings on a same tragedy drama movie. One of them indicated his or her emotional state as "happy" when finishing the Recommender systems (RS) are an effective way in alleviating movie, because this user thought it was a really good movie. By information overload by tailoring recommendations to users’ per- contrast, another user may express his or her feeling as "sad" since sonal preferences. Context-aware recommender systems (CARS) the user was impressed or moved by the tragedy movie. As a result, take contextual factors (such as time, location, companion, occa- the two users have same rating behaviors on the movie but with sion, etc) into account in modeling user profiles and in generating different emotional reactions or expressions. One of the potential recommendations. For example, users’ choice on movies may be reasons is that different user personalities may result in different children very different if the user is going to watch the movie with ways or habits for users to express their emotions. partner rather than with his or her . Therefore, the users’ rating profiles associated with different (or any information that can be Context, is usually defined as, " even opposed) emotional reactions therefore could be useful to used to characterize the situation of an entity. An entity is assist recommendations. In this paper, we propose to incorporate a person, place, or object that is considered relevant to the emotional reactions (or expressions) as regularization terms in the interaction between a user and an application, including the user context-aware matrix factorization approach, and further explore and applications themselves [12]". In CARS, we view the dynamic its effect on the performance of context-aware recommendations. The following sections are organized as follows: Section 2 introduces related work, including the background of context- aware recommendation, and the role of emotions and personality in recommender systems. Section 3 gives the preliminary description of essential information, such as the LDOS-CoMoDa movie data which contains emotional variables, and the introduction about the CAMF technique. Section 4 discusses our methodology
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