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Related papers: A Scalable Framework for Automatic Playlist Contin…

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The RecSys Challenge 2018 focused on automatic playlist continuation, i.e., the task was to recommend additional music tracks for playlists based on the playlist's title and/or a subset of the tracks that it already contains. The challenge…

Information Retrieval · Computer Science 2018-08-14 Irene Teinemaa , Niek Tax , Carlos Bentes

Recommending playlists to users in the context of a digital music service is a difficult task because a playlist is often more than the mere sum of its parts. We present a novel method for generating playlist embeddings that are invariant…

Information Retrieval · Computer Science 2020-06-23 Brett Vintch

Music streaming services often leverage sequential recommender systems to predict the best music to showcase to users based on past sequences of listening sessions. Nonetheless, most sequential recommendation methods ignore or…

Information Retrieval · Computer Science 2024-08-30 Viet-Anh Tran , Guillaume Salha-Galvan , Bruno Sguerra , Romain Hennequin

This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer. Track Mix automatically generates "mix" playlists inspired by initial music tracks, allowing users to…

User-generated item lists are popular on many platforms. Examples include video-based playlists on YouTube, image-based lists (or"boards") on Pinterest, book-based lists on Goodreads, and answer-based lists on question-answer forums like…

Information Retrieval · Computer Science 2020-01-01 Yun He , Yin Zhang , Weiwen Liu , James Caverlee

Recommender Systems are an integral part of music sharing platforms. Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. The systems which aim at increasing the…

Information Retrieval · Computer Science 2018-11-21 Noveen Sachdeva , Kartik Gupta , Vikram Pudi

Recommender systems play an essential role in music streaming services, prominently in the form of personalized playlists. Exploring the user interactions within these listening sessions can be beneficial to understanding the user…

Information Retrieval · Computer Science 2019-04-24 Sainath Adapa

The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender…

Machine Learning · Computer Science 2022-10-04 Darius Afchar , Alessandro B. Melchiorre , Markus Schedl , Romain Hennequin , Elena V. Epure , Manuel Moussallam

Every generation throws a hero up the pop charts. For the current generation, one of the most relevant pop charts is the Spotify Top 200. Spotify is the world's largest music streaming service and the Top 200 is a daily list of the…

Social and Information Networks · Computer Science 2019-10-04 Michelangelo Harris , Brian Liu , Cean Park , Ravi Ramireddy , Gloria Ren , Max Ren , Shangdi Yu , Andrew Daw , Jamol Pender

We investigate algorithmic collective action in transformer-based recommender systems. Our use case is a music streaming platform where a collective of fans aims to promote the visibility of an underrepresented artist by strategically…

Information Retrieval · Computer Science 2025-01-17 Joachim Baumann , Celestine Mendler-Dünner

Spotify's streaming charts offer a real-time lens into music popularity, driving discovery, playlists, and even revenue potential. Understanding what influences a song's rise in ranks on these charts-especially early on-can guide marketing…

Sound · Computer Science 2025-08-19 Ian Jacob Cabansag , Paul Ntegeka

Recommender systems have become indispensable in music streaming services, enhancing user experiences by personalizing playlists and facilitating the serendipitous discovery of new music. However, the existing recommender systems overlook…

Information Retrieval · Computer Science 2023-08-29 Yunhak Oh , Sukwon Yun , Dongmin Hyun , Sein Kim , Chanyoung Park

The automated generation of music playlists can be naturally regarded as a sequential task, where a recommender system suggests a stream of songs that constitute a listening session. In order to predict the next song in a playlist, some of…

Information Retrieval · Computer Science 2018-07-13 Andreu Vall , Massimo Quadrana , Markus Schedl , Gerhard Widmer

In this paper, we propose a pipeline to generate contextualized list recommendations with descriptive shelves in the domain of audiobooks. By creating several shelves for topics the user has an affinity to, e.g. Uplifting Women's Fiction,…

The music streaming service Deezer extensively relies on its Flow algorithm, which generates personalized radio-style playlists of songs, to help users discover musical content. Nonetheless, despite promising results over the past years,…

Information Retrieval · Computer Science 2022-07-25 Théo Bontempelli , Benjamin Chapus , François Rigaud , Mathieu Morlon , Marin Lorant , Guillaume Salha-Galvan

Users are able to access millions of songs through music streaming services like Spotify, Pandora, and Deezer. Access to such large catalogs, created a need for relevant song recommendations. However, user preferences are highly subjective…

Information Retrieval · Computer Science 2020-09-08 Boning Gong , Mesut Kaya , Nava Tintarev

Recommendation systems have become essential in modern music streaming platforms, shaping how users discover and engage with songs. One common approach in recommendation systems is collaborative filtering, which suggests content based on…

Information Retrieval · Computer Science 2025-07-04 Terence Zeng , Abhishek K. Umrawal

Our analysis reviews and visualizes the audio features and popularity of songs streamed on Spotify*. Our dataset, downloaded from Kaggle and originally sourced from Spotify API, consists of multiple Excel files containing information…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-08-06 Virginia Ochi , Ricardo Estrada , Teezal Gaji , Wendy Gadea , Emily Duong

Media services providers, such as music streaming platforms, frequently leverage swipeable carousels to recommend personalized content to their users. However, selecting the most relevant items (albums, artists, playlists...) to display in…

Machine Learning · Computer Science 2020-10-01 Walid Bendada , Guillaume Salha , Théo Bontempelli

Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast…

Information Retrieval · Computer Science 2020-08-27 Diego Sánchez-Moreno , Yong Zheng , María N. Moreno-García