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

User Privacy on Spotify: Predicting Personal Data from Music Preferences

openThe way we listen to music has changed drastically in the past decade. Now we can play any kind of music from various artists around the world through our smart devices. Many music streaming providers, if not most, are built with systems to track users’ music preferences and suggest new content. The music we listen to reveals a great deal about who we are. In general, people share their playlists and songs of their favorite artists on the music platform; find people with common music genres and connect with them. It is not always easy to make friends with unknown people, but music is a good way to accomplish that. In spite of that, we must also look at other sides of the coin from a security perspective. Is it a good idea to share music interests with others or will it compromise our privacy? According to privacy experts and developers, there is no purposeless data. Everything can be used to infer private information, even a single like on social media, which seems, at first sight, meaningless, but it can reveal more information than it promises. In the case that our musical tastes reveal our information, we may be profiled for targeted advertisement, by surveillance agencies, or in general, become potential victims of malicious activities Since music is part of our daily lives, and there are many providers that let us listen to music, we are even more at risk of being profiled and having our data sold. In this research, we demonstrate the feasibility of inferring personal data based on playlists and songs people publicly shared on Spotify. Through an online survey, we collected a new dataset containing the private information of 750 Spotify users and we downloaded around 402,999 songs extracted from a total of 8777 playlists. Our statistical analysis shows significant correlations between users’ music preferences (e.g., music genre) and private information (e.g., age, gender, economic status). As a consequence of significant correlations, we built several machine-learning models to infer private information and our results demonstrated that such inference is possible, posing a real privacy threat to all music listeners. In particular, we accurately predicted the gender (71.7% f1-score), and several other private attributes, such as whether a person drinks (62.8% f1-score) or smokes (60.2% f1-score) regularly. The purpose of this project is to raise awareness about how seemingly purposeless data can reveal personal information and educate users about how to better protect their privacy.The way we listen to music has changed drastically in the past decade. Now we can play any kind of music from various artists around the world through our smart devices. Many music streaming providers, if not most, are built with systems to track users’ music preferences and suggest new content. The music we listen to reveals a great deal about who we are. In general, people share their playlists and songs of their favorite artists on the music platform; find people with common music genres and connect with them. It is not always easy to make friends with unknown people, but music is a good way to accomplish that. In spite of that, we must also look at other sides of the coin from a security perspective. Is it a good idea to share music interests with others or will it compromise our privacy? According to privacy experts and developers, there is no purposeless data. Everything can be used to infer private information, even a single like on social media, which seems, at first sight, meaningless, but it can reveal more information than it promises. In the case that our musical tastes reveal our information, we may be profiled for targeted advertisement, by surveillance agencies, or in general, become potential victims of malicious activities Since music is part of our daily lives, and there are many providers that let us listen to music, we are even more at risk of being profiled and having our data sold. In this research, we demonstrate the feasibility of inferring personal data based on playlists and songs people publicly shared on Spotify. Through an online survey, we collected a new dataset containing the private information of 750 Spotify users and we downloaded around 402,999 songs extracted from a total of 8777 playlists. Our statistical analysis shows significant correlations between users’ music preferences (e.g., music genre) and private information (e.g., age, gender, economic status). As a consequence of significant correlations, we built several machine-learning models to infer private information and our results demonstrated that such inference is possible, posing a real privacy threat to all music listeners. In particular, we accurately predicted the gender (71.7% f1-score), and several other private attributes, such as whether a person drinks (62.8% f1-score) or smokes (60.2% f1-score) regularly. The purpose of this project is to raise awareness about how seemingly purposeless data can reveal personal information and educate users about how to better protect their privac

Environmental Changes and the Dynamics of Musical Identity

Musical tastes reflect our unique values and experiences, our relationships with others, and the places where we live. But as each of these things changes, do our tastes also change to reflect the present, or remain fixed, reflecting our past? Here, we investigate how where a person lives shapes their musical preferences, using geographic relocation to construct quasi-natural experiments that measure short- and long-term effects. Analyzing comprehensive data on over 16 million users on Spotify, we show that relocation within the United States has only a small impact on individuals' tastes, which remain more similar to those of their past environments. We then show that the age gap between a person and the music they consume indicates that adolescence, and likely their environment during these years, shapes their lifelong musical tastes. Our results demonstrate the robustness of individuals' musical identity, and shed new light on the development of preferences.Comment: Accepted to be published at ICWSM'1

Simulating activities: Relating motives, deliberation, and attentive coordination

Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems

Chapter Introduction and Overview

Drawing on perspectives from music psychology, cognitive neuroscience, philosophy, musicology, clinical psychology, and music education, Music and Mental Imagery provides a critical overview of cutting-edge research on the various types of mental imagery associated with music. The four main parts cover an introduction to the different types of mental imagery associated with music such as auditory/musical, visual, kinaesthetic, and multimodal mental imagery; a critical assessment of established and novel ways to measure mental imagery in various musical contexts; coverage of different states of consciousness, all of which are relevant for, and often associated with, mental imagery in music, and a critical overview of applications of mental imagery in health, educational ,and performance settings. By both critically reviewing up-to-date scientific research and offering new empirical results, this book provides a unique overview of the different types and origins of mental imagery in musical contexts, various ways to measure them, and intriguing insights into related mental phenomena such as mind-wandering and synaesthesia. This will be of particular interest for scholars and researchers of music psychology and music education. It will also be useful for practitioners working with music in applied health and educational contexts

El diseño de materiales para el desarrollo de la Competencia Intercultural Comunicativa en el aula de ILE

This present paper explores, analyses, proves and tries to provide a solution to the patent need for Intercultural Communicative Competence Development that a group of 56 students of first year of post-compulsory education portray. Thus, trough the convergence of different fields of study ranging from Intercultural Communicative Competence, Communicative Language Teaching and Second Language Acquisition, this document presents a needs analysis and a subsequent pedagogical intervention proposal that aims to erode the existing prejudices in young students today and to promote intercultural interaction by raising their awareness and by providing them with the necessary tools to reflect on themselves and on the others in a more tolerant, thoughtful and communicative way. The pedagogical intervention proposal has been nominated as “Ten Mini-Pills for Intercultural Communicative Competence Development” in which students are faced with concrete examples of the elements that shape other cultures so as for them to be able to deconstruct their cosmovision and thus integrate the reality of others. The “Ten Mini-Pills for Intercultural Communicative Competence Development” have been designed in the light of adaptability and easy implementation, so as for them to not only meet the needs of this concrete group of students but of as many as possible. <br /

Prediction, evolution and privacy in social and affiliation networks

In the last few years, there has been a growing interest in studying online social and affiliation networks, leading to a new category of inference problems that consider the actor characteristics and their social environments. These problems have a variety of applications, from creating more effective marketing campaigns to designing better personalized services. Predictive statistical models allow learning hidden information automatically in these networks but also bring many privacy concerns. Three of the main challenges that I address in my thesis are understanding 1) how the complex observed and unobserved relationships among actors can help in building better behavior models, and in designing more accurate predictive algorithms, 2) what are the processes that drive the network growth and link formation, and 3) what are the implications of predictive algorithms to the privacy of users who share content online. The majority of previous work in prediction, evolution and privacy in online social networks has concentrated on the single-mode networks which form around user-user links, such as friendship and email communication. However, single-mode networks often co-exist with two-mode affiliation networks in which users are linked to other entities, such as social groups, online content and events. We study the interplay between these two types of networks and show that analyzing these higher-order interactions can reveal dependencies that are difficult to extract from the pair-wise interactions alone. In particular, we present our contributions to the challenging problems of collective classification, link prediction, network evolution, anonymization and preserving privacy in social and affiliation networks. We evaluate our models on real-world data sets from well-known online social networks, such as Flickr, Facebook, Dogster and LiveJournal