"More of an art than a science": Supporting the creation of playlists and mixes

This paper presents an analysis of how people construct playlists and mixes. Interviews with practitioners and postings made to a web site are analyzed using a grounded theory approach to extract themes and categorizations. The information sought is often encapsulated as music information retrieval tasks, albeit not as the traditional "known item search" paradigm. The collated data is analyzed and trends identified and discussed in relation to music information retrieval algorithms that could help support such activity

Graph-RAT: Combining data sources in music recommendation systems

The complexity of music recommendation systems has increased rapidly in recent years, drawing upon different sources of information: content analysis, web-mining, social tagging, etc. Unfortunately, the tools to scientifically evaluate such integrated systems are not readily available; nor are the base algorithms available. This article describes Graph-RAT (Graph-based Relational Analysis Toolkit), an open source toolkit that provides a framework for developing and evaluating novel hybrid systems. While this toolkit is designed for music recommendation, it has applications outside its discipline as well. An experiment—indicative of the sort of procedure that can be configured using the toolkit—is provided to illustrate its usefulness

Automatic Personalized Playlist Generation

Käesolevas magistritöös on esitatud automaatse personaliseeritud pleilisti tekitaja probleemi lähenemisviiside uuring. Lisaks teoreetilise tausta lühiülevaatele me dokumenteerisime oma lähenemist: meie poolt tehtud katsed ning nende tulemused. Meie algoritm koosneb kahest põhiosast: pleilisti hindamisfunktsiooni konstrueerimine ning pleilisti genereerimisstrateegia valik. Esimese ülesande lahendamiseks on valitud Naive Bayes klassifitseerija ning 5-elemendiline MIRtoolbox tööristakasti poolt kavandatud audio sisupõhiste attribuutide vektor, mis klassiitseerivad pleilisti heaks või halvaks 82% täpsusega - palju parem kui juhuslik klassifitseerija (50%). Teise probleemi lahendamiseks proovisime kolm genereerimisalgoritmi: lohistus (Shuffle), randomiseeritud otsing (Randomized Search) ning geneetiline algoritm (Genetic Algorithm). Vastavalt katsete tulemustele kõige paremini ja kiiremini töötab randomiseeritud otsingu algoritm. Kõik katsed on tehtud 5 ning 10 elemendilistel pleilistidel. Kokkuvõttes, oleme arendanud automatiseeritud personaliseeritud pleilisti tekitaja algoritmi, mis vastavalt meie hinnangutele vastab ka kasutaja ootustele rohkem, kui juhuslikud lohistajad. Algoritmi võib kasutada keerulisema pleilistide konstrueerimiseks

Automatic Music Playlist Generation via Simulation-based Reinforcement Learning

Personalization of playlists is a common feature in music streaming services, but conventional techniques, such as collaborative filtering, rely on explicit assumptions regarding content quality to learn how to make recommendations. Such assumptions often result in misalignment between offline model objectives and online user satisfaction metrics. In this paper, we present a reinforcement learning framework that solves for such limitations by directly optimizing for user satisfaction metrics via the use of a simulated playlist-generation environment. Using this simulator we develop and train a modified Deep Q-Network, the action head DQN (AH-DQN), in a manner that addresses the challenges imposed by the large state and action space of our RL formulation. The resulting policy is capable of making recommendations from large and dynamic sets of candidate items with the expectation of maximizing consumption metrics. We analyze and evaluate agents offline via simulations that use environment models trained on both public and proprietary streaming datasets. We show how these agents lead to better user-satisfaction metrics compared to baseline methods during online A/B tests. Finally, we demonstrate that performance assessments produced from our simulator are strongly correlated with observed online metric results.Comment: 10 pages. KDD 2

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

NextOne Player: A Music Recommendation System Based on User Behavior.

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대조학습을 통한 콘텐츠 기반 음악 추천에서의 비선호도 반영

학위논문(석사) -- 서울대학교대학원 : 융합과학기술대학원 지능정보융합학과, 2023. 2. 이교구.Advanced music recommendation systems are being introduced along with the development of machine learning. However, it is essential to design a music recommendation system that can increase user satisfaction by understanding users music tastes, not by the complexity of models. Although several studies related to music recommendation systems exploiting negative preferences have shown performance improvements, there was a lack of explanation on how they led to better recommendations. In this work, we analyze the role of negative preference in users music tastes by comparing music recommendation models with contrastive learning exploiting prefer- ence (CLEP) but with three different training strategies - exploiting preferences of both positive and negative (CLEP-PN), positive only (CLEP-P), and negative only (CLEP- N). We evaluate the effectiveness of the negative preference by validating each system with a small amount of personalized data obtained via survey and further illuminate the possibility of exploiting negative preference in music recommendations. Our experimental results show that CLEP-N outperforms the other two in accuracy and false positive rate. Furthermore, the proposed training strategies produced a consistent tendency regardless of different types of front-end musical feature extractors, proving the stability of the proposed method.머신러닝의 발전과 함께 이를 활용한 다양한 음악 추천 시스템이 도입되고 있 다. 그러나 음악 추천 시스템에 대한 사용자의 만족도를 높이기 위해서는 단순히 복잡하고 성능이 좋은 모델을 적용하는 것이 아닌, 사용자의 음악 취향에 대한 이해 가 반영된 음악 추천 시스템을 설계해야 한다. 비선호도를 활용한 음악 추천 시스템 역시 여러 연구에서 제안되었는데, 비선호도를 반영함으로써 성능이 향상됨을 보였 지만 비선호도를 반영하는 것이 구체적으로 어떻게 더 나은 추천으로 이어졌는지에 대한 설명은 부족했다. 본 연구를 통해 우리는 선호도와 비선호도를 다르게 적용하여 훈련된 대조 학습 모델(Contrastive Learning Exploiting Preference, CLEP)을 비교 분석함으로써 사용 자의 음악 취향에서 비선호도가 어떤 역할을 가지는지에 대해 알아보고자 한다. 본 연구에서 소개하는 모델은 반영하고자 하는 선호도에 따라 다르게 학습되는 세 가 지 모델을 선호도와 비선호도를 모두 반영한 모델(CLEP-PN), 선호도만을 반영한 모델(CLEP-P), 비선호도만을 반영한 모델(CLEP-N)로 나뉜다. 본연구에서제안한각모델의훈련및평가를위해서설문조사를통해개인선호 도가 포함된 소량의 데이터셋을 구축하였다. 구축한 데이터셋에 대해 각 모델들의 평가 결과를 비교하여 음악 취향에서의 비선호도의 특징과 음악 추천 시스템에서 비선호도를 활용할 수 있는 가능성에 대해 추가로 조명한다. 또한, 음악 데이터로부터 특징을 추출하는 과정에서 사전 학습된 서로 다른 세 가지 모델을 이용하였으며, 특징 추출기와 무관하게 일관된 경향성의 결과를 보여 제안 방법의 안정성을 입증 하였다.1 Introduction 6 1.1 Motivation 6 1.2 Research Questions 9 2 Background 11 2.1 Background Theories 11 2.1.1 Recommender Systems 11 2.1.2 Music Recommendation System 14 2.1.3 Contrastive Learning 16 2.2 Related Works 17 2.2.1 Content-based Music Recommendation 17 2.2.2 Recommendation Systems Exploiting Negative Preference 20 3 Methods 22 3.1 Feature Extraction 22 3.1.1 Contrastive Learning of Musical Representations 24 3.1.2 Music Effects Encoder 25 3.1.3 Jukebox 25 3.2 Contrastive Learning Exploiting Preference (CLEP) 26 3.3 Preference Prediction 29 4 Experiments 30 4.1 Experimental Setups 30 4.2 User Preference Dataset 31 4.3 Evaluation 35 4.3.1 Evaluation Metric 35 4.3.2 Experimental Results 37 5 Results and Discussion 43 6 Conclusion 48 6.1 Contribution 48 6.1.1 Novel Approach on Content-Based Music Recommendation 49 6.1.2 Comprehension of Music Preference 51 6.2 Limitation and Future Works 51석

On-demand music streaming and its effects on music piracy

In the late 1990’s, music industry revenues began to decline, mostly due to the proliferation of the Internet which enabled consumers to easily pirate music. Record companies and artists began fighting legal battles and investing in educational campaigns in an attempt to teach young people the value of intellectual property. However, the times are now starting to change. In 2016, US retail revenues from recorded music grew 11,4%, the biggest increment since 1998. Streaming revenues have now surpassed income from the sale of traditional formats. Nevertheless, there is still a big player in the market worth paying attention to: music piracy. This thesis seeks to investigate the impact on-demand streaming services have been having on illegal downloading and uncover young music consumers’ habits and preferences. Through an online survey, the study used a conjoint analysis to uncover consumers’ preference structure. It also included direct questions to assess music consumers’ characteristics and habits. The results show that ethics and perceived risk negatively influence the decision to pirate music. On the other hand, higher ethics and involvement are associated with the propensity to pay for streaming services. Also, as age increases the propensity to pay for streaming rises and the tendency to pirate decreases. Even though consumers are price sensitive, price is not always the main decision factor. Finally, we observe that streaming did in fact help to reduce the incidence of music piracy among young music consumers.No final dos anos 90, as receitas da indústria da música começaram a diminuir, principalmente devido à proliferação da Internet, que permitia aos consumidores piratear música facilmente. Discográficas e artistas começaram a travar disputas jurídicas e a investir em campanhas educacionais na tentativa de ensinar aos jovens o valor da propriedade intelectual. No entanto, os tempos estão a mudar. Em 2016, a receita associada à música cresceu 11,4%, o maior incremento desde 1998. As receitas de streaming já ultrapassaram as receitas dos formatos tradicionais. No entanto, ainda há um grande player no mercado ao qual vale a pena prestar atenção: a pirataria. A presente tese procura investigar o impacto que os serviços de streaming têm tido na pirataria de música, e compreender os hábitos e preferências dos jovens consumidores de música. Num questionário on-line, o estudo recorreu a uma análise conjoint para desvendar a estrutura de preferências dos consumidores. Também incluiu perguntas que permitiram avaliar as características e hábitos dos consumidores. Os resultados demonstram que a ética e o risco influenciam negativamente a decisão de piratear música. Por outro lado, maior ética e envolvimento estão associados a uma maior propensão a pagar por streaming. Além disso, à medida que a idade aumenta, a propensão a pagar por streaming aumenta, e a tendência para piratear diminui. Apesar dos consumidores serem sensíveis ao preço, este nem sempre é o principal fator de decisão. Finalmente, observamos que o streming ajudou a reduzir a incidência da pirataria entre os jovens consumidores de música