Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Social Collaborative Retrieval

Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many potential applications that involve aspects of both recommendation and information retrieval, and the task of collaborative retrieval---a combination of these two traditional problems---has recently been introduced. Successful collaborative retrieval requires overcoming severe data sparsity, making additional sources of information, such as social graphs, particularly valuable. In this paper we propose a new model for collaborative retrieval, and show that our algorithm outperforms current state-of-the-art approaches by incorporating information from social networks. We also provide empirical analyses of the ways in which cultural interests propagate along a social graph using a real-world music dataset.Comment: 10 page

Community music: history and current practice, its constructions of ‘community’, digital turns and future soundings

The UK has been a pivotal national player within the development of community music practice. In the UK community music developed broadly from the 1960s and had a significant burgeoning period in the 1980s. Community music nationally and internationally has gone on to build a set of practices, a repertoire, an infrastructure of organisations, qualifications and career paths. There are elements of cultural and debatably pedagogic innovations in community music. These have to date only partly been articulated and historicised within academic research. This document brings together and reviews research under the headings of history and definitions; practice; repertoire; community; pedagogy; digital technology; health and therapy; policy and funding, and impact and evaluation. A 90-entry, 22,000 word annotated bibliography was also produced (McKay and Higham 2011). An informed group of 15 practitioners and academics reviewed the authors’ initial findings at a knowledge exchange colloquium and advised on further investigation. Some of the gaps in research identified are: an authoritative history, an examination of repertoire, the relationship with other music (practice), the freelance practitioner career, evidence of impact and value, the potential for a pedagogy