72 research outputs found
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
A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services
Music streaming services often aim to recommend songs for users to extend the
playlists they have created on these services. However, extending playlists
while preserving their musical characteristics and matching user preferences
remains a challenging task, commonly referred to as Automatic Playlist
Continuation (APC). Besides, while these services often need to select the best
songs to recommend in real-time and among large catalogs with millions of
candidates, recent research on APC mainly focused on models with few
scalability guarantees and evaluated on relatively small datasets. In this
paper, we introduce a general framework to build scalable yet effective APC
models for large-scale applications. Based on a represent-then-aggregate
strategy, it ensures scalability by design while remaining flexible enough to
incorporate a wide range of representation learning and sequence modeling
techniques, e.g., based on Transformers. We demonstrate the relevance of this
framework through in-depth experimental validation on Spotify's Million
Playlist Dataset (MPD), the largest public dataset for APC. We also describe
how, in 2022, we successfully leveraged this framework to improve APC in
production on Deezer. We report results from a large-scale online A/B test on
this service, emphasizing the practical impact of our approach in such a
real-world application.Comment: Accepted as a Full Paper at the SIGIR 2023 conferenc
Track Mix Generation on Music Streaming Services using Transformers
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
discover music similar to their favorite content. To generate these mixes, we
consider a Transformer model trained on millions of track sequences from user
playlists. In light of the growing popularity of Transformers in recent years,
we analyze the advantages, drawbacks, and technical challenges of using such a
model for mix generation on the service, compared to a more traditional
collaborative filtering approach. Since its release, Track Mix has been
generating playlists for millions of users daily, enhancing their music
discovery experience on Deezer.Comment: RecSys 2023 - Industry track with oral presentatio
An ensemble approach of recurrent neural networks using pre-trained embeddings for playlist completion
This paper describes the approach of the D2KLab team to the RecSys Challenge 2018 that focuses on the task of playlist completion. We propose an ensemble strategy of different recurrent neural networks leveraging pre-trained embeddings representing tracks, artists, albums, and titles as inputs. We also use lyrics from which we extract semantic and stylistic features that we fed into the network for the creative track. The RNN learns a probabilistic model from the sequences of items in the playlist, which is then used to predict the most likely tracks to be added to the playlist. Concerning the playlists without tracks, we implemented a fall-back strategy called Title2Rec that generates recommendations using only the playlist title. We optimized the RNN, Title2Rec, and the ensemble approach on a validation set, tuning hyper-parameters such as the optimizer algorithm, the learning rate, and the generation strategy. This approach is effective in predicting tracks for a playlist and flexible to include diverse types of inputs, but it is also computationally demanding in the training phase
Automated Playlist Continuation with Apache PredictionIO
The Minrva project team, a software development research group based at the University of Illinois Library, developed a data-focused recommender system to participate in the creative track of the 2018 ACM RecSys Challenge, which focused on music recommendation. We describe here the large-scale data processing the Minrva team researched and developed for foundational reconciliation of the Million Playlist Dataset using external authority data on the web (e.g. VIAF, WikiData). The secondary focus of the research was evaluating and adapting the processing tools that support data reconciliation. This paper reports on the playlist enrichment process, indexing, and subsequent recommendation model developed for the music recommendation challenge.Ope
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