Managing the impact of the pandemic in the music industry

Corona virus came to shake the whole music industry, it stopped all live events which consequently limited the profits of musicians, labels and all other professionals related to this area. Suddenly only streaming could guarantee a steady source of income for all the workers in this industry. A Partial Least Squares model of survey shows that the Pandemic Induced Behaviour positively affected user satisfaction, user loyalty and willingness to pay either in an indirect or direct way. We believe the pandemic crisis contributed for the growth of streaming and it will be felt in the long run.Através do método matemático "Partial Least Squares" (PLS), esta tese pretende analisar a lealdade do cliente e a satisfação do mesmo perante o serviço em questão ("streaming"), mediados através do comportamento induzido pela pandemia bem como a vontade do próprio cliente em pagar pelo serviço, visto que a pandemia levou ao confinamento da população que ficou apenas com "streaming" como alternativa aos concertos/festivais daí especularmos que a pandemia tenha efeitos positivos neste mercado. Concordantemente, os resultados do PLS de 197 perspetivas individuais e independentes, indicaram que a maioria das hipóteses predefinidas eram significantes, especialmente, a ligação entre a lealdade do cliente e a satisfação do cliente, a ligação entre a satisfação do cliente e a vontade de pagar, bem como a relação entre o comportamento induzido pela pandemia e a vontade de pagar ou a satisfação do cliente. Por outro lado, o efeito indireto entre o comportamento induzido pela pandemia na vontade de pagar do cliente através da sua satisfação e lealdade não foi provado significante como tínhamos antecipado

User-controllable personalization: A case study with SetFusion

In this research we investigated the role of user controllability on personalized systems by implementing and studying a novel interactive recommender interface, SetFusion. We examined whether allowing the user to control the process of fusing or integrating different algorithms (i.e., different sources of relevance) resulted in increased engagement and a better user experience. The essential contribution of this research stems from the results of a user study (N=40) of controllability in a scenario where users could fuse different recommendation approaches, with the possibility of inspecting and filtering the items recommended. First, we introduce an interactive Venn diagram visualization, which combined with sliders, can provide an efficient visual paradigm for information filtering. Second, we provide a three-fold evaluation of the user experience: objective metrics, subjective user perception, and behavioral measures. Through the analysis of these metrics, we confirmed results from recent studies, such as the effect of trusting propensity on accepting the recommendations and also unveiled the importance of features such as being a native speaker. Our results present several implications for the design and implementation of user-controllable personalized systems

USER CONTROLLABILITY IN A HYBRID RECOMMENDER SYSTEM

Since the introduction of Tapestry in 1990, research on recommender systems has traditionally focused on the development of algorithms whose goal is to increase the accuracy of predicting users’ taste based on historical data. In the last decade, this research has diversified, with human factors being one area that has received increased attention. Users’ characteristics, such as trusting propensity and interest in a domain, or systems’ characteristics, such as explainability and transparency, have been shown to have an effect on improving the user experience with a recommender. This dissertation investigates on the role of controllability and user characteristics upon the engagement and experience of users of a hybrid recommender system. A hybrid recommender is a system that integrates the results of different algorithms to produce a single set of recommendations. This research examines whether allowing the user to control the process of fusing or integrating different algorithms (i.e., different sources of relevance) results in increased engagement and a better user experience. The essential contribution of this dissertation is an extensive study of controllability in a hybrid fusion scenario. In particular, the introduction of an interactive Venn diagram visualization, combined with sliders explored in a previous work, can provide an efficient visual paradigm for information filtering with a hybrid recommender that fuses different prospects of relevance with overlapping recommended items. This dissertation also provides a three-fold evaluation of the user experience: objective metrics, subjective user perception, and behavioral measures

IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS

Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent. Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance. To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available

Interactive Latent Space for Mood-Based Music Recommendation

The way we listen to music has been changing fundamentally in past two decades with the increasing availability of digital recordings and portability of music players. Up to date research in music recommendation attracted millions of users to online, music streaming services, containing tens of millions of tracks (e.g. Spotify, Pandora). The main focus of up to date research in recommender systems has been algorithmic accuracy and optimization of ranking metrics. However, recent work has highlighted the importance of other aspects of the recommendation process, including explanation, transparency, control and user experience in general. Building on these aspects, this dissertation explores user interaction, control and visual explanation of music related mood metadata during recommendation process. It introduces a hybrid recommender system that suggests music artists by combining mood-based and audio content filtering in a novel interactive interface. The main vehicle for exploration and discovery in music collection is a novel visualization that maps moods and artists in the same, latent space, built upon reduced dimensions of high-dimensional artist-mood associations. It is not known what the reduced dimensions represent and this work uses hierarchical mood model to explain the constructed space. Results of two user studies, with over 200 participants each, show that visualization and interaction in a latent space improves acceptance and understanding of both metadata and item recommendations. However, too much of either can result in cognitive overload and a negative impact on user experience. The proposed visual mood space and interactive features, along with the aforementioned findings, aim to inform design of future interactive recommendation systems