The Music Streaming Sessions Dataset

At the core of many important machine learning problems faced by online streaming services is a need to model how users interact with the content. These problems can often be reduced to a combination of 1) sequentially recommending items to the user, and 2) exploiting the user's interactions with the items as feedback for the machine learning model. Unfortunately, there are no public datasets currently available that enable researchers to explore this topic. In order to spur that research, we release the Music Streaming Sessions Dataset (MSSD), which consists of approximately 150 million listening sessions and associated user actions. Furthermore, we provide audio features and metadata for the approximately 3.7 million unique tracks referred to in the logs. This is the largest collection of such track metadata currently available to the public. This dataset enables research on important problems including how to model user listening and interaction behaviour in streaming, as well as Music Information Retrieval (MIR), and session-based sequential recommendations.Comment: 3 pages, introducing a new large scale datase

Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations

In this paper, we introduce a psychology-inspired approach to model and predict the music genre preferences of different groups of users by utilizing human memory processes. These processes describe how humans access information units in their memory by considering the factors of (i) past usage frequency, (ii) past usage recency, and (iii) the current context. Using a publicly available dataset of more than a billion music listening records shared on the music streaming platform Last.fm, we find that our approach provides significantly better prediction accuracy results than various baseline algorithms for all evaluated user groups, i.e., (i) low-mainstream music listeners, (ii) medium-mainstream music listeners, and (iii) high-mainstream music listeners. Furthermore, our approach is based on a simple psychological model, which contributes to the transparency and explainability of the calculated predictions.Comment: Dominik Kowald and Elisabeth Lex contributed equally to this wor

Fairness in music recommender systems: a stakeholder-centered mini review

The performance of recommender systems highly impacts both music streaming platform users and the artists providing music. As fairness is a fundamental value of human life, there is increasing pressure for these algorithmic decision-making processes to be fair as well. However, many factors make recommender systems prone to biases, resulting in unfair outcomes. Furthermore, several stakeholders are involved, who may all have distinct needs requiring different fairness considerations. While there is an increasing interest in research on recommender system fairness in general, the music domain has received relatively little attention. This mini review, therefore, outlines current literature on music recommender system fairness from the perspective of each relevant stakeholder and the stakeholders combined. For instance, various works address gender fairness: one line of research compares differences in recommendation quality across user gender groups, and another line focuses on the imbalanced representation of artist gender in the recommendations. In addition to gender, popularity bias is frequently addressed; yet, primarily from the user perspective and rarely addressing how it impacts the representation of artists. Overall, this narrative literature review shows that the large majority of works analyze the current situation of fairness in music recommender systems, whereas only a few works propose approaches to improve it. This is, thus, a promising direction for future research

Ripple Knowledge Graph Convolutional Networks For Recommendation Systems

Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy. This paper introduces an end-to-end deep learning model, named RKGCN, which dynamically analyses each user's preferences and makes a recommendation of suitable items. It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs. RKGCN is able to offer more personalized and relevant recommendations in three different scenarios. The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies, books, and music

A Cross-Country Investigation of User Connection Patterns in Online Social Networks

Given the global expansion, the borderless nature, and the social impact of social media, this paper provides an examination of users’ connection patterns in online social networks, more specifically the users’ cross-country connection patterns. We study three highly different social media platforms, Facebook, Last.fm, and 500px, and approach two main research questions: First, we set out to answer which countries’ social media users are mainly connected with users within their own country; and which countries are characterized by a wide spectrum of cross-country (transnational) user connections. In doing so, we also identify the “attractor” countries, being characterized by alluring a large portion of users from other countries to connect to users in the respective attractor country. Second, we compare the results between the three social media platforms under investigation and analyze and discuss differences in the cross-country connection patterns. Third, we investigate whether countries’ attractor values are correlated with cultural features (according to Hofstede). Our results contribute to understanding the complex social ties between people and how they are reflected in connection behavior on social media