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

Towards Psychometrics-based Friend Recommendations in Social Networking Services

Two of the defining elements of Social Networking Services are the social profile, containing information about the user, and the social graph, containing information about the connections between users. Social Networking Services are used to connect to known people as well as to discover new contacts. Current friend recommendation mechanisms typically utilize the social graph. In this paper, we argue that psychometrics, the field of measuring personality traits, can help make meaningful friend recommendations based on an extended social profile containing collected smartphone sensor data. This will support the development of highly distributed Social Networking Services without central knowledge of the social graph.Comment: Accepted for publication at the 2017 International Conference on AI & Mobile Services (IEEE AIMS

Measuring perfectionism in sport, dance, and exercise: Review, critique, recommendations

Over the past 25 years, a number of multidimensional measures of perfectionism has been developed. Based on different models of multidimensional perfectionism, these measures contain different numbers of subscales, and most of the time the different subscales bear different names. This presents a confusing situation to researchers unfamiliar with the often complex details of the perfectionism literature who want to conduct research on perfectionism in sport, dance, and exercise and need to make a decision as to what measure to use to capture individual differences in multidimensional perfectionism. The aim of the present chapter is to give researchers some guidance in this decision. To this aim, the chapter will (a) review the available multidimensional measures that have been published in international peer-reviewed journals and (b) provide a critique of these measures. In addition, the chapter will provide (c) recommendations on which measures to use and guidance on which decisions researchers have to make when using these measures to capture perfectionism in sport, dance, and exercise

Personality in Computational Advertising: A Benchmark

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person’s buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user’s experience than generic parameters, accurate predictions reveal important aspects of user’s attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer’s buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users’ personality factors and 1,200 personal users’ pictures

Some Things Are Better Left Unseen: Toward More Effective Communication And Team Performance In Video-Mediated Interactions

By default, most video-mediated communication systems show the user his or her own video feed, yet there is no prior research to show if this helps or hinders communication. In general, virtual teams desire richer media to improve team interaction. However, in this case more information may not be helpful. Drawing on Objective Self Awareness theory in social psychology and theories of cognitive overload from communication, hypotheses are proposed concerning how viewing oneself influences virtual team interaction. It is argued that viewing oneself will lead to lower team performance and other negative outcomes. The hypotheses are tested in a laboratory experiment, manipulating whether participants were able to view their own feeds during video-mediated communication. The results suggest that viewing oneself leads to a reduction in team performance and individual satisfaction. The findings, in terms of several theoretical explanations, and implications for managers and systems designers are discussed in the paper