Modelling Sequential Music Track Skips using a Multi-RNN Approach

Modelling sequential music skips provides streaming companies the ability to better understand the needs of the user base, resulting in a better user experience by reducing the need to manually skip certain music tracks. This paper describes the solution of the University of Copenhagen DIKU-IR team in the 'Spotify Sequential Skip Prediction Challenge', where the task was to predict the skip behaviour of the second half in a music listening session conditioned on the first half. We model this task using a Multi-RNN approach consisting of two distinct stacked recurrent neural networks, where one network focuses on encoding the first half of the session and the other network focuses on utilizing the encoding to make sequential skip predictions. The encoder network is initialized by a learned session-wide music encoding, and both of them utilize a learned track embedding. Our final model consists of a majority voted ensemble of individually trained models, and ranked 2nd out of 45 participating teams in the competition with a mean average accuracy of 0.641 and an accuracy on the first skip prediction of 0.807. Our code is released at https://github.com/Varyn/WSDM-challenge-2019-spotify.Comment: 4 page

Predicting Session Length in Media Streaming

Session length is a very important aspect in determining a user's satisfaction with a media streaming service. Being able to predict how long a session will last can be of great use for various downstream tasks, such as recommendations and ad scheduling. Most of the related literature on user interaction duration has focused on dwell time for websites, usually in the context of approximating post-click satisfaction either in search results, or display ads. In this work we present the first analysis of session length in a mobile-focused online service, using a real world data-set from a major music streaming service. We use survival analysis techniques to show that the characteristics of the length distributions can differ significantly between users, and use gradient boosted trees with appropriate objectives to predict the length of a session using only information available at its beginning. Our evaluation on real world data illustrates that our proposed technique outperforms the considered baseline.Comment: 4 pages, 3 figure

트랜스포머 기반 음악 스트리밍 세션 추천 시스템

학위논문(석사) -- 서울대학교대학원 : 데이터사이언스대학원 데이터사이언스학과, 2022. 8. 신효필.Recommendation systems have grown in popularity over the last few years, with the rise of big data and development of computing resources. Compared to simple rule based methods or content based filtering methods used for recommendation during the early development stage of recommendation systems, recent methodologies try to implement much more complex models. Latent factor models and collaborative filtering methods were developed to find similarities between users and items without actually knowing their characteristics, and gained popularity. Various item domains, mainly movie and retail, have extensively used these recommendation algorithms. With the development of deep learning architectures, various deep learning based recommendation systems emerged in recent years. While a lot of them were focused on generating the predicted item ratings when given a big data comprised of user ids, item ids, and ratings, there were some efforts to generate next-item recommendations as well. Next-item recommendations receive a session or sequence of actions by some user, and try to predict the next action of a user. NVIDIA recently used Transformers, a deep learning architecture in the field of Natural Language Processing (NLP), to build a session based recommendation system called Transformers4Rec. The system showed state of the art performances for the usual movie and retail domains. In the music domain, unfortunately, advanced models for session-based recommendations have been explored to a small extent. Therefore, this thesis will attempt to apply Transformer based architectures to session-based recommendation for music streaming, by utilizing a dataset from Spotify and framework from NVIDIA. In this thesis, unique characteristics of music data that validates this research’s purpose are explored. The effectiveness of Transformer architectures on music data are shown with next-item prediction performances on actual user streaming session data, and methods for feature engineering and data preprocessing to ensure the best prediction results are investigated. An empirical analysis that compares various Transformer architectures is also provided, with models further analyzed with additional feature information.최근 트랜스포머 기반 추천시스템들이 다양한 분야에서 높은 성능을 보여왔다. 하지만 음악 스트리밍 분야에는 적용되지 않았었고, 이 논문을 통해 음악 스트리밍 분야에 트랜스포머 기반 세션 추천시스템이 어떤 성능을 보여주는지 탐색해 보았다. 데이터 전처리를 통해 유저들이 음악을 실제로 좋아해서 들었을 법한 세션들만 남기려 노력했고, 세션 기반 추천시스템에 맞게 데이터를 정제했다. 음악과 관련된 다양한 정보들도 모델 훈련에 반영하기 위해 카테고리 형태로 바꿔주었고, 훈련 자체는 세션 기반 추천시스템에서 자주 쓰이는 점진적 훈련법을 활용했다. 최종 실험 결과에서는 데이터의 비정제성과 비밀집성을 극복하고 비슷한 데이터셋과 경쟁력을 갖추는 성과를 보여주었다. 이 연구를 통해 음악 스트리밍 세션 추천시스템에 트랜스포머 기반 모델이라는 새로운 가능성을 보여 주었고, 추후 연구자들이 참고할 수 있는 시작점을 제공하였다.1 Introduction 1 1.1 Research Topic 1 1.2 Purpose of Research 2 1.3 Need for Research 3 1.3.1 Recent Trends 3 1.3.2 Dataset Characteristics 4 2 Related Works 9 2.1 Overview of NLP and RecSys 9 2.2 Past Works on Incorporating Features 12 3 Methodology 13 3.1 Music Streaming Sessions Dataset 13 3.2 Music Recommendation Model 14 3.2.1 NVTabular 15 3.2.2 Transformers4Rec 15 3.3 Feature Embeddings 16 3.4 Session Information 17 3.5 Transformer Architectures 18 3.6 Metrics 19 4 Experiments 21 4.1 Data Preprocessing 21 4.2 Embedding 23 4.2.1 No features 23 4.2.2 Session features 23 4.2.3 Song features 25 4.3 Hyperparameters 27 4.4 Training 28 4.4.1 Problem Statement 28 4.4.2 Pipeline 28 4.4.3 Incremental Training, Evaluation 29 4.5 Results 30 4.5.1 Simple item IDs 30 4.5.2 Item IDs + Session Information 31 4.5.3 Item IDs + Session Information + Track Metadata 32 5 Conclusion and Future Works 34 Bibliography 36 초 록 41석