Social software for music

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

A hybrid recommender system for improving automatic playlist continuation

Although widely used, the majority of current music recommender systems still focus on recommendations’ accuracy, userpreferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations’ quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address “similar concepts” rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items’ discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs’ similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.This work has been partially supported by the Catalan Agency for Management of University and Research Grants (AGAUR) (2017 SGR 574), by the European Regional Development Fund (ERDF), through the Incentive System to Research and Technological development, within the Portugal2020 Competitiveness and Internationalization Operational Program –COMPETE 2020– (POCI-01-0145-FEDER006961), and by the Portuguese Foundation for Science and Technology (FCT) (UID/EEA/50014/2013).Peer ReviewedPostprint (author's final draft

A hybrid approach for item collection recommendations : an application to automatic playlist continuation

Current recommender systems aim mainly to generate accurate item recommendations, without properly evaluating the multiple dimensions of the recommendation problem. However, in many domains, like in music, where items are rarely consumed in isolation, users would rather need a set of items, designed to work well together, while having some cognitive properties as a whole, related to their perception of quality and satisfaction. In this thesis, a hybrid case-based recommendation approach for item collections is proposed. In particular, an application to automatic playlist continuation, addressing similar cognitive concepts, rather than similar users, is presented. Playlists, that are sets of music items designed to be consumed as a sequence, with a specific purpose and within a specific context, are treated as cases. The proposed recommender system is based on a meta-level hybridization. First, Latent Dirichlet Allocation is applied to the set of past playlists, described as distributions over music styles, to identify their underlying concepts. Then, for a started playlist, its semantic characteristics, like its latent concept and the styles of the included items, are inferred, and Case-Based Reasoning is applied to the set of past playlists addressing the same concept, to construct and recommend a relevant playlist continuation. A graph-based item model is used to overcome the semantic gap between songs’ signal-based descriptions and users’ high-level preferences, efficiently capture the playlists’ structures and the similarity of the music items in those. As the proposed method bases its reasoning on previous playlists, it does not require the construction of complex user profiles to generate accurate recommendations. Furthermore, apart from relevance, support to parameters beyond accuracy, like increased coherence or support to diverse items is provided to deliver a more complete user experience. Experiments on real music datasets have revealed improved results, compared to other state of the art techniques, while achieving a “good trade-off” between recommendations’ relevance, diversity and coherence. Finally, although actually focusing on playlist continuations, the designed approach could be easily adapted to serve other recommendation domains with similar characteristics.Los sistemas de recomendación actuales tienen como objetivo principal generar recomendaciones precisas de artículos, sin evaluar propiamente las múltiples dimensiones del problema de recomendación. Sin embargo, en dominios como la música, donde los artículos rara vez se consumen en forma aislada, los usuarios más bien necesitarían recibir recomendaciones de conjuntos de elementos, diseñados para que se complementaran bien juntos, mientras se cubran algunas propiedades cognitivas, relacionadas con su percepción de calidad y satisfacción. En esta tesis, se propone un sistema híbrido de recomendación meta-nivel, que genera recomendaciones de colecciones de artículos. En particular, el sistema se centra en la generación automática de continuaciones de listas de música, tratando conceptos cognitivos similares, en lugar de usuarios similares. Las listas de reproducción son conjuntos de elementos musicales diseñados para ser consumidos en secuencia, con un propósito específico y dentro de un contexto específico. El sistema propuesto primero aplica el método de Latent Dirichlet Allocation a las listas de reproducción, que se describen como distribuciones sobre estilos musicales, para identificar sus conceptos. Cuando se ha iniciado una nueva lista, se deducen sus características semánticas, como su concepto y los estilos de los elementos incluidos en ella. A continuación, el sistema aplica razonamiento basado en casos, utilizando las listas del mismo concepto, para construir y recomendar una continuación relevante. Se utiliza un grafo que modeliza las relaciones de los elementos, para superar el ?salto semántico? existente entre las descripciones de las canciones, normalmente basadas en características sonoras, y las preferencias de los usuarios, expresadas en características de alto nivel. También se utiliza para calcular la similitud de los elementos musicales y para capturar la estructura de las listas de dichos elementos. Como el método propuesto basa su razonamiento en las listas de reproducción y no en usuarios que las construyeron, no se requiere la construcción de perfiles de usuarios complejos para poder generar recomendaciones precisas. Aparte de la relevancia de las recomendaciones, el sistema tiene en cuenta parámetros más allá de la precisión, como mayor coherencia o soporte a la diversidad de los elementos para enriquecer la experiencia del usuario. Los experimentos realizados en bases de datos reales, han revelado mejores resultados, en comparación con las técnicas utilizadas normalmente. Al mismo tiempo, el algoritmo propuesto logra un "buen equilibrio" entre la relevancia, la diversidad y la coherencia de las recomendaciones generadas. Finalmente, aunque la metodología presentada se centra en la recomendación de continuaciones de listas de reproducción musical, el sistema se puede adaptar fácilmente a otros dominios con características similares.Postprint (published version

Advances in next-track music recommendation

Technological advances in the music industry have dramatically changed how people access and listen to music. Today, online music stores and streaming services offer easy and immediate means to buy or listen to a huge number of songs. One traditional way to find interesting items in such cases when a vast amount of choices are available is to ask others for recommendations. Music providers utilize correspondingly music recommender systems as a software solution to the problem of music overload to provide a better user experience for their customers. At the same time, an enhanced user experience can lead to higher customer retention and higher business value for music providers. Different types of music recommendations can be found on today's music platforms, such as Spotify or Deezer. Providing a list of currently trending music, finding similar tracks to the user's favorite ones, helping users discover new artists, or recommending curated playlists for a certain mood (e.g., romantic) or activity (e.g., driving) are examples of common music recommendation scenarios. "Next-track music recommendation" is a specific form of music recommendation that relies mainly on the user's recently played tracks to create a list of tracks to be played next. Next-track music recommendations are used, for instance, to support users during playlist creation or to provide personalized radio stations. A particular challenge in this context is that the recommended tracks should not only match the general taste of the listener but should also match the characteristics of the most recently played tracks. This thesis by publication focuses on the next-track music recommendation problem and explores some challenges and questions that have not been addressed in previous research. In the first part of this thesis, various next-track music recommendation algorithms as well as approaches to evaluate them from the research literature are reviewed. The recommendation techniques are categorized into the four groups of content-based filtering, collaborative filtering, co-occurrence-based, and sequence-aware algorithms. Moreover, a number of challenges, such as personalizing next-track music recommendations and generating recommendations that are coherent with the user's listening history are discussed. Furthermore, some common approaches in the literature to determine relevant quality criteria for next-track music recommendations and to evaluate the quality of such recommendations are presented. The second part of the thesis contains a selection of the author's publications on next- track music recommendation as follows. 1. The results of comprehensive analyses of the musical characteristics of manually created playlists for music recommendation; 2. the results of a multi-dimensional comparison of different academic and commercial next-track recommending techniques; 3. the results of a multi-faceted comparison of different session-based recommenders, among others, for the next-track music recommendation problem with respect to their accuracy, popularity bias, catalog coverage as well as computational complexity; 4. a two-phase approach to recommend accurate next-track recommendations that also match the characteristics of the most recent listening history; 5. a personalization approach based on multi-dimensional user models that are extracted from the users' long-term preferences; 6. a user study with the aim of determining the quality perception of next-track music recommendations generated by different algorithms

Matchmakers or tastemakers? Platformization of cultural intermediation & social media’s engines for ‘making up taste’

There are long-standing practices and processes that have traditionally mediated between the processes of production and consumption of cultural content. The prominent instances of these are: curating content by identifying and selecting cultural content in order to promote to a particular set of audiences; measuring audience behaviours to construct knowledge about their tastes; and guiding audiences through recommendations from cultural experts. These cultural intermediation processes are currently being transformed, and social media platforms play important roles in this transformation. However, their role is often attributed to the work of users and/or recommendation algorithms. Thus, the processes through which data about users’ taste are aggregated and made ready for algorithmic processing are largely neglected. This study takes this problematic as an important gap in our understanding of social media platforms’ role in the transformation of cultural intermediation. To address this gap, the notion of platformization is used as a theoretical lens to examine the role of users and algorithms as part of social media’s distinct data-based sociotechnical configuration, which is built on the so-called ‘platform-logic’. Based on a set of conceptual ideas and the findings derived through a single case study on a music discovery platform, this thesis developed a framework to explain ‘platformization of cultural intermediation’. This framework outlines how curation, guidance, and measurement processes are ‘plat-formed’ in the course of development and optimisation of a social media platform. This is the main contribution of the thesis. The study also contributes to the literature by developing the concept of social media’s engines for ‘making up taste’. This concept illuminates how social media operate as sociotechnical cultural intermediaries and participates in tastemaking in ways that acquire legitimacy from the long-standing trust in the objectivity of classification, quantification, and measurement processes

ECLAP 2012 Conference on Information Technologies for Performing Arts, Media Access and Entertainment

It has been a long history of Information Technology innovations within the Cultural Heritage areas. The Performing arts has also been enforced with a number of new innovations which unveil a range of synergies and possibilities. Most of the technologies and innovations produced for digital libraries, media entertainment and education can be exploited in the field of performing arts, with adaptation and repurposing. Performing arts offer many interesting challenges and opportunities for research and innovations and exploitation of cutting edge research results from interdisciplinary areas. For these reasons, the ECLAP 2012 can be regarded as a continuation of past conferences such as AXMEDIS and WEDELMUSIC (both pressed by IEEE and FUP). ECLAP is an European Commission project to create a social network and media access service for performing arts institutions in Europe, to create the e-library of performing arts, exploiting innovative solutions coming from the ICT

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