Content-Based Multimedia Recommendation Systems: Definition and Application Domains

The goal of this work is to formally provide a general definition of a multimedia recommendation system (MMRS), in particular a content-based MMRS (CB-MMRS), and to shed light on different applications of multimedia content for solving a variety of tasks related to recommendation. We would like to disambiguate the fact that multimedia recommendation is not only about recommending a particular media type (e.g., music, video), rather there exists a variety of other applications in which the analysis of multimedia input can be usefully exploited to provide recommendations of various kinds of information

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

A user-centered investigation of personal music tours

Streaming services use recommender systems to surface the right music to users. Playlists are a popular way to present music in a list-like fashion, i.e. as a plain list of songs. An alternative are tours, where the songs alternate with segues, which explain the connections between consecutive songs. Tours address the user need of seeking background information about songs, and are found to be superior to playlists, given the right user context. In this work, we provide, for the first time, a user-centered evaluation of two tour-generation algorithms (Greedy and Optimal) using semi-structured interviews. We assess the algorithms, we discuss attributes of the tours that the algorithms produce, we identify which attributes are desirable and which are not, and we enumerate several possible improvements to the algorithms, along with practical suggestions on how to implement the improvements. Our main findings are that Greedy generates more likeable tours than Optimal, and that three important attributes of tours are segue diversity, song arrangement and song familiarity. More generally, we provide insights into how to present music to users, which could inform the design of user-centered recommender systems

Empirical and modelling approaches to the psychology of musical awe

The experience of awe is one that we may all recognise, the feeling of being overwhelmed by a great force and often inspired from its presence. Research from various disciplines has recently taken an interest in understanding and explaining this phenomenon through psychological models guided by the extensive philosophical discourse on the sublime. This research project, through its seven studies, takes some of the first steps to validate and test these psychological models through participant-based experiences of awe evoked from music, known as ‘musical awe’. Studies 1 and 2 examined first-hand accounts of musical awe, and found commonalities in experiences, associated judgements, and emotional sentiments through a mixed methods approach. Studies 3, 4, and 5 used the Musical Awe Corpus (MAC)—a collection of music excerpts from experiences of musical awe in Study 1—to examine shared musical associations and categorical groupings through online psychometric scale experiments. Study 6 investigated the relationship between judgements of size and associations of musical awe through a time-series, virtual object manipulation task. Lastly, Study 7 used music information retrieval techniques to extract and analyse musical features from the MAC to assess which features are prevalent and important to awe-associate music. Taken together, the findings of these studies advance a general characterisation of musical awe as an emotional phenomenon best understood as shared between two valenced-differentiated groups. The more common group is associated with positive valence and emotions of joy and wonder, whilst the other group is associated with threat and fear. However, both groups of musical awe show strong associations with size and power. Sonically, awe-related music, especially the negative valenced group, can be characterised by high levels of brightness, roughness, and extensive changes in spectral qualities. In conclusion, these studies largely support much of the theoretical grounding concerning the production and occurrence of awe, and from these findings, a novel empirically driven model was created to expand and improve our understanding of musical awe