Surprise

About: Surprise is a research topic. Over the lifetime, 4371 publications have been published within this topic receiving 99386 citations.

Emotion-Induced Engagement in Internet Video Advertisements

Abstract: This study shows how advertisers can leverage emotion and attention to engage consumers in watching Internet video advertisements. In a controlled experiment, the authors assessed joy and surprise through automated facial expression detection for a sample of advertisements. They assessed concentration of attention through eye tracking and viewer retention by recording zapping behavior. This allows tests of predictions about the interplay of these emotions and interperson attention differences at each point in time during exposure. Surprise and joy effectively concentrate attention and retain viewers. However, importantly, the level rather than the velocity of surprise affects attention concentration most, whereas the velocity rather than the level of joy affects viewer retention most. The effect of joy is asymmetric, with higher gains for increases than losses for decreases. Using these findings, the authors develop representative emotion trajectories to support ad design and testing.

Facial emotion recognition using multi-modal information

Abstract: Facial emotion recognition will become vitally important in future multi-cultural visual communication systems, for emotion translation between cultures, which may be considered analogous to speech translation. However so far the recognition of facial emotions is mainly addressed by computer vision researchers, based on facial display. Also detection of vocal expressions of emotions can be found in research work done by acoustic researchers. Most of these research paradigms are devoted purely to visual or purely to auditory human emotion detection. However we found that it is very interesting to consider both these auditory and visual information together, for processing, since we hope this kind of multi-modal information processing will become a datum of information processing in future multimedia era. By several intensive subjective evaluation studies we found that human beings recognise anger, happiness, surprise and dislike by their visual appearance, compared to voice only detection. When the audio track of each emotion clip is dubbed with a different type of auditory emotional expression, still anger, happiness and surprise were video dominant. However the dislike emotion gave mixed responses to different speakers. In both studies we found that sadness and fear emotions were audio dominant. As a conclusion, we propose a method of facial emotion detection by using a hybrid approach, which uses multi-modal information for facial emotion recognition.

Classification as Culture: Types and Trajectories of Music Genres

Abstract: Questions of symbolic classification have been central to sociology since its earliest days, given the relevance of distinctions for both affiliation and conflict. Music and its genres are no exception, organizing people and songs within a system of symbolic classification. Numerous studies chronicle the history of specific genres of music, but none document recurrent processes of development and change across musics. In this article, we analyze 60 musics in the United States, delineating between 12 social, organizational, and symbolic attributes. We find four distinct genre types—Avant-garde, Scene-based, Industry-based, and Traditionalist. We also find that these genre types combine to form three distinct trajectories. Two-thirds originate in an Avant-garde genre, and the rest originate as a scene or, to our surprise, in an Industry-based genre. We conclude by discussing a number of questions raised by our findings, including the implications for understanding symbolic classification in fields other tha...

Bias and Debias in Recommender System: A Survey and Future Directions

Abstract: While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, etc. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and perform debiasing when necessary. When reviewing the papers that consider biases in RS, we find that, to our surprise, the studies are rather fragmented and lack a systematic organization. The terminology "bias" is widely used in the literature, but its definition is usually vague and even inconsistent across papers. This motivates us to provide a systematic survey of existing work on RS biases. In this paper, we first summarize seven types of biases in recommendation, along with their definitions and characteristics. We then provide a taxonomy to position and organize the existing work on recommendation debiasing. Finally, we identify some open challenges and envision some future directions, with the hope of inspiring more research work on this important yet less investigated topic.