scholarly journals Investigating country-specific music preferences and music recommendation algorithms with the LFM-1b dataset

2017 ◽  
Vol 6 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Markus Schedl
Keyword(s):  
Music Recommendation ◽  
Music Preferences ◽  
Recommendation Algorithms ◽  
Country Specific

scholarly journals Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes

2021 ◽  
Vol 3 ◽  
Author(s):  
Markus Schedl ◽  
Christine Bauer ◽  
Wolfgang Reisinger ◽  
Dominik Kowald ◽  
Elisabeth Lex
Keyword(s):  
Recommendation System ◽  
State Of The Art ◽  
Music Listening ◽  
Context Aware ◽  
Music Recommendation ◽  
Music Preferences ◽  
User Models ◽  
Gating Mechanism ◽  
Country Clusters ◽  
Country Specific

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the “music mainstream” strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervized learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user’s country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-à-vis state-of-the-art algorithms that do not exploit this type of context information.

Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity

2011 ◽  
pp. 203-221 ◽  
Author(s):  
Zehra Cataltepe ◽  
Berna Altinel
Keyword(s):  
Demographic Data ◽  
Recommendation Systems ◽  
Personal Data ◽  
Music Recommendation ◽  
Music Preferences ◽  
User Models ◽  
Online Music ◽  
Important Field ◽  
Case Based

As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative, content-based and case-based recommendation systems and their hybrids have been used for music recommendation. There are already a number of online music recommendation systems. Although specific user information, such as, demographic data, education, and origin have been shown to affect music preferences, they are usually not collected by the online music recommendation systems, because users would not like to disclose their personal data. Therefore, user models mostly contain information about which music pieces a user liked and which ones s/he did not and when.

scholarly journals Music Recommendation Algorithms: Discovering Weekly or Discovering Weakly?

2019 ◽  
Author(s):  
Jennie Silber
Keyword(s):  
User Experience ◽  
Music Recommendation ◽  
Media Communication ◽  
Recommendation Algorithms ◽  
Potential Benefits ◽  
Cultural Reality ◽  
Winner Take All ◽  
User Actions ◽  
Music Streaming ◽  
The Way

This thesis analyzes and assesses the cultural impact and economic viability that the top music streaming platforms have on the consumption and discovery of music, with a specific focus on recommendation algorithms. Through the support of scholarly and journalistic research as well as my own user experience, I evaluate the known constructs that fuel algorithmic recommendations, but also make educated inferences about the variables concealed from public knowledge. One of the most significant variables delineated throughout this thesis is the power held by human curators and the way they interact with algorithms to frame and legitimize content. Additionally, I execute my own experiment by creating new user profiles on the two streaming platforms popularly used for the purpose of discovery, Spotify and SoundCloud, and record each step of the music discovery process experienced by a new user. After listening to an equal representation of all genre categories within each platform, I then compare the genre, release year, artist status, and content promotion gathered from my listening history to the algorithmically-generated songs listed in my ‘Discover Weekly’ and ‘SoundCloud Weekly’ personalized playlists. The results from this experiment demonstrate that the recommendation algorithms that power these discovery playlists intrinsically facilitate the perpetuation of a stardriven, “winner-take-all” marketplace, where new, popular, trendy, music is favored, despite how diverse of a selection the music being listened to is.The content of this thesis is significant to understanding the culture of music streaming and is also contributory to the field of media communication. Unlike any other scholarly research, the “walk-through” experiment uniquely tracks a new user experience through the cognizant application of user actions and directly assesses the factors that challenge successful music recommendation. This method of research specifically highlights the influence that music streaming platforms have not only as tastemakers, but more importantly, as gatekeepers of cultural information, shaping the perceived value and relevance of artists and genres through recommendation. This thesis underlines the challenges faced by recommendation systems in providing the novel, yet relevant recommendations necessary to satisfy the needs of users, while also providing wide-ranging, yet representative recommendations to stimulate diversity and creativity within society.These challenges include the subjective organization of songs and genres within a platform’s interface, the misrepresentation of songs and artists within genre-based playlists, the use of user actions (skips, likes, dislikes, passive listening, drifting, etc.) as an assertion of one’s likes and dislikes, as well as the manipulation of hit-producing market trends. This thesis delves deeply into each challenge and the ways they affect the inaccuracy, subjectivity, and homogeneity currently projected through music streaming recommendations. Lastly, this thesis addresses the potential benefits and apprehensions of future contextually aware technology and its ability to reshape the way recommendation algorithms gather and process user listening data. Ultimately, my hope is that this research sheds light on the responsibility of music listeners, but more importantly, of music distributors and curators, as taste makers and gatekeepers, to act progressively and ethically in constructing the cultural reality we live in.

scholarly journals Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

2021 ◽  
Author(s):  
Zhisheng Yang ◽  
Jinyong Cheng
Keyword(s):  
Deep Learning ◽  
Auxiliary Information ◽  
Substantial Improvement ◽  
User Preference ◽  
Knowledge Graph ◽  
Historical Records ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Recommendation Algorithms

Abstract In recommendation algorithms, data sparsity and cold start problems are always inevitable. In order to solve such problems, researchers apply auxiliary information to recommendation algorithms to mine and obtain more potential information through users' historical records and then improve recommendation performance. This paper proposes a model ST_RippleNet, which combines knowledge graph with deep learning. In this model, users' potential interests are mined in the knowledge graph to stimulate the propagation of users' preferences on the set of knowledge entities. In the propagation of preferences, we adopt a triple-based multi-layer attention mechanism, and the distribution of users' preferences for candidate items formed by users' historical click information is used to predict the final click probability. In ST_RippleNet model, music data set is added to the original movie and book data set, and the improved loss function is applied to the model, which is optimized by RMSProp optimizer. Finally, tanh function is added to predict click probability to improve recommendation performance. Compared with the current mainstream recommendation methods, ST_RippleNet recommendation algorithm has very good performance in AUC and ACC, and has substantial improvement in movie, book and music recommendation.

Factorizing Historical User Actions for Next-Day Purchase Prediction

2022 ◽  
Vol 16 (1) ◽  
pp. 1-26
Author(s):  
Bang Liu ◽  
Hanlin Zhang ◽  
Linglong Kong ◽  
Di Niu
Keyword(s):  
User Behavior ◽  
Purchase Behavior ◽  
Time Decay ◽  
Music Recommendation ◽  
Unified Framework ◽  
Transaction Data ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Real World Datasets ◽  
User Actions

It is common practice for many large e-commerce operators to analyze daily logged transaction data to predict customer purchase behavior, which may potentially lead to more effective recommendations and increased sales. Traditional recommendation techniques based on collaborative filtering, although having gained success in video and music recommendation, are not sufficient to fully leverage the diverse information contained in the implicit user behavior on e-commerce platforms. In this article, we analyze user action records in the Alibaba Mobile Recommendation dataset from the Alibaba Tianchi Data Lab, as well as the Retailrocket recommender system dataset from the Retail Rocket website. To estimate the probability that a user will purchase a certain item tomorrow, we propose a new model called Time-decayed Multifaceted Factorizing Personalized Markov Chains (Time-decayed Multifaceted-FPMC), taking into account multiple types of user historical actions not only limited to past purchases but also including various behaviors such as clicks, collects and add-to-carts. Our model also considers the time-decay effect of the influence of past actions. To learn the parameters in the proposed model, we further propose a unified framework named Bayesian Sparse Factorization Machines. It generalizes the theory of traditional Factorization Machines to a more flexible learning structure and trains the Time-decayed Multifaceted-FPMC with the Markov Chain Monte Carlo method. Extensive evaluations based on multiple real-world datasets demonstrate that our proposed approaches significantly outperform various existing purchase recommendation algorithms.

Enhancing Music Recommendation Algorithms Using Cultural Metadata

2005 ◽  
Vol 34 (2) ◽  
pp. 161-172 ◽  
Author(s):  
Stephan Baumann ◽  
Oliver Hummel
Keyword(s):  
Music Recommendation ◽  
Recommendation Algorithms

scholarly journals TRUST IN THE MUSIC? AUTOMATED MUSIC DISCOVERY, MUSIC RECOMMENDATION SYSTEMS & ALGORITHMIC CULTURE.

2019 ◽  
Vol 2019 ◽  
Author(s):  
Freeman Sophie Olivia
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Digital Culture ◽  
Music Recommendation ◽  
Private Lives ◽  
Complex Element ◽  
Recommendation Algorithms ◽  
Music Consumption ◽  
Music Streaming ◽  
Music Recommendation System

In this paper I argue that music recommendation algorithms are a complex element of contemporary digital culture. We trust music streaming and recommender systems like Spotify to ‘set the mood’ for us, to soundtrack our private lives and activities, to recommend & discover for us. These systems purport to ‘know’ us (alongside the millions of other users), and as such we let them into our most intimate listening spaces and moments. We fetishise and share the datafication of our listening habits, reflected to us annually in Spotify’s “Your 2018 Wrapped” and every Monday in ‘Discover Weekly’, even daily in the “playlists made for you”. As the accuracy of these recommendations increases, so too does our trust in these systems. ‘Bad’ or inaccurate recommendations feel like a betrayal, giving us the sense that the algorithms don’t really know us at all. Users speak of ‘their’ algorithm, as if it belonged to them and not a part of a complex machine learning recommendation system. This paper builds on research which critically examined the music recommendation system that powers Spotify and its many discovery features. The research explored the process through which Spotify automates discovery by incorporating established methods of music consumption, and demonstrated that music recommendation systems such as Spotify are emblematic of the politics of algorithmic culture.

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