Feel the Moosic: Emotion-based Music Selection and Recommendation

Digital transformation has changed all aspects of life, including the music market and listening habits. The spread of mobile devices and music streaming services has enabled the possibility to access a huge selection of music regardless of time or place. However, this access leads to the customer\u27s problem of choosing the right music for a certain situation or mood. The user is often overwhelmed while choosing music. Context information, especially the emotional state of the user, can help within this process. The possibilities of an emotional music selection are currently limited. The providers rely on predefined playlists for different situations or moods. However, the problem with these lists is, that they do not adapt to new user conditions. A simple, intuitive and automatic emotion-based music selection has so far been poorly investigated in IS practice and research. This paper describes the IS music research project Moosic , which investigates and iteratively implements an intuitive emotion-based music recommendation application. In addition, an initial evaluation of the prototype will be discussed and an outlook on further development will be given

Affective Recommendation of Movies Based on Selected Connotative Features

The apparent difficulty in assessing emotions elicited by movies and the undeniable high variability in subjects emotional responses to filmic content have been recently tackled by exploring film connotative properties: the set of shooting and editing conventions that help in transmitting meaning to the audience. Connotation provides an intermediate representation which exploits the objectivity of audiovisual descriptors to predict the subjective emotional reaction of single users. This is done without the need of registering users physiological signals neither by employing other people highly variable emotional rates, but just relying on the inter-subjectivity of connotative concepts and on the knowledge of users reactions to similar stimuli. This work extends previous by extracting audiovisual and film grammar descriptors and, driven by users rates on connotative properties, creates a shared framework where movie scenes are placed, compared and recommended according to connotation. We evaluate the potential of the proposed system by asking users to assess the ability of connotation in suggesting filmic content able to target their affective requests

Sequence-based context-aware music recommendation

© 2017, Springer Science+Business Media, LLC. Contextual factors greatly affect users’ preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users’ preferences for music, and then recommends music pieces that fit their real-time requirements. Specifically, the proposed approach first learns the low dimensional representations of music pieces from users’ music listening sequences using neural network models. Based on the learned representations, it then infers and models users’ general and contextual preferences for music from users’ historical listening records. Finally, music pieces in accordance with user’s preferences are recommended to the target user. Extensive experiments are conducted on real world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse data

Why Consumers Pay Voluntarily: Evidence from Online Music

Customers at the online music label Magnatune can pay what they want for albums, as long as the payment is within a given price range ($5-$18). Magnatune recommends to pay $8, and on average customers paid$8.20 (Regner and Barria, 2009). We ran an online survey and collected responses from 227 frequent Magnatune customers to gain insights about the underlying motivations to pay more than necessary. We control for individual response- and sample selection-bias, and find that reciprocity and guilt appear to be the major drivers for generous voluntary payments. Being inclined to follow social norms is a positive determinant for payments around the recommended price.social preferences, other-regarding behaviour, music industry, reciprocity, guilt, social norms, altruism, fairness, social-image concerns, survey

PROMOTING HEALTH EDUCATION- CHANGING LIFESTYLES

According to Health Facts published by Ministry of Health Malaysia (MOH) in 2010 most non communicable disease (NCD) consists of diseases that are implications of unhealthy lifestle are the main causes of death in Malaysia. This translates into the need of educating citizens on how to have a healthy lifestyle to avoid the risks of getting fatal NCDs. A Heahh Education Division has already been set up as one of the 5 division under the Public Health Department Ministry of Heahh Malaysia that could be seen through their various websites. The issue is the lack effectiveness of the website to educate regardless of the increasing number of online users. Thus, this research is planned to effectively conduct health education to reach the online citizens by the use of Human Computer Interaction theories as well as a learning theories. The main objective ofthis project is to implement these two theories in the development of the website is to create awareness to the community on the importance of living a healthy life as well as the implications of doing otherwise. The content of the website includes the main criteria of lifestyle such as hygiene, nutrition and physical activities and the knowledge of how to conduct a healthy lifestyle base on the criteria, and the implications of not doing so. The website also displays basic information regarding the disease which includes the causes of the disease, the implications to the body, the implication towards the life of a patient infected with the disease, and what can be done to prevent the disease, as well as what can be done to help people infected with the disease. The educating and learning process is also equipped with risk assessments of diseases that would act as a decision support system to the users. The result of this project is the increase of interest and awareness regarding the importance and knowledge of having a healthy lifestyle among the internet users. The conclusion of this research project is conducting health education online is effective if the right theories and techniques are applied, as well as taking into account the user's perspective

Music Genre Classification with ResNet and Bi-GRU Using Visual Spectrograms

Music recommendation systems have emerged as a vital component to enhance user experience and satisfaction for the music streaming services, which dominates music consumption. The key challenge in improving these recommender systems lies in comprehending the complexity of music data, specifically for the underpinning music genre classification. The limitations of manual genre classification have highlighted the need for a more advanced system, namely the Automatic Music Genre Classification (AMGC) system. While traditional machine learning techniques have shown potential in genre classification, they heavily rely on manually engineered features and feature selection, failing to capture the full complexity of music data. On the other hand, deep learning classification architectures like the traditional Convolutional Neural Networks (CNN) are effective in capturing the spatial hierarchies but struggle to capture the temporal dynamics inherent in music data. To address these challenges, this study proposes a novel approach using visual spectrograms as input, and propose a hybrid model that combines the strength of the Residual neural Network (ResNet) and the Gated Recurrent Unit (GRU). This model is designed to provide a more comprehensive analysis of music data, offering the potential to improve the music recommender systems through achieving a more comprehensive analysis of music data and hence potentially more accurate genre classification

Predicting Popularity of Hedonic Digital Content via Artificial Intelligence Imagery Analysis of Thumbnails

Hedonic digital content backs a wide variety of business models. Yet, due to its experience good nature, consumers cannot assess its value before consumption. To overcome this obstacle, thumbnail images are frequently employed to provide an experience of content, and trigger views and sales. In spite of fragmented evidence from human-computer interaction research, thumbnails largely constitute a black box for research and practice. This research aims to fill this gap and asks: How and why do basic, conceptual and social features of thumbnail images affect popularity of hedonic digital content? To answer the question, we employ artificial intelligence imagery analysis to test and confirm a variance model against evidence from 400,000 YouTube videos. Our findings entail important theoretical contributions to visual perception in online contexts. In addition, this research proposes artificial intelligence imagery analysis as a new and fruitful research method for the largely visual information systems discipline