20 research outputs found
Adaptive Submodular Influence Maximization with Myopic Feedback
This paper examines the problem of adaptive influence maximization in social
networks. As adaptive decision making is a time-critical task, a realistic
feedback model has been considered, called myopic. In this direction, we
propose the myopic adaptive greedy policy that is guaranteed to provide a (1 -
1/e)-approximation of the optimal policy under a variant of the independent
cascade diffusion model. This strategy maximizes an alternative utility
function that has been proven to be adaptive monotone and adaptive submodular.
The proposed utility function considers the cumulative number of active nodes
through the time, instead of the total number of the active nodes at the end of
the diffusion. Our empirical analysis on real-world social networks reveals the
benefits of the proposed myopic strategy, validating our theoretical results.Comment: Accepted by IEEE/ACM International Conference Advances in Social
Networks Analysis and Mining (ASONAM), 201
A Degeneracy Framework for Scalable Graph Autoencoders
In this paper, we present a general framework to scale graph autoencoders
(AE) and graph variational autoencoders (VAE). This framework leverages graph
degeneracy concepts to train models only from a dense subset of nodes instead
of using the entire graph. Together with a simple yet effective propagation
mechanism, our approach significantly improves scalability and training speed
while preserving performance. We evaluate and discuss our method on several
variants of existing graph AE and VAE, providing the first application of these
models to large graphs with up to millions of nodes and edges. We achieve
empirically competitive results w.r.t. several popular scalable node embedding
methods, which emphasizes the relevance of pursuing further research towards
more scalable graph AE and VAE.Comment: International Joint Conference on Artificial Intelligence (IJCAI
2019
Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged
as powerful node embedding methods. In particular, graph AE and VAE were
successfully leveraged to tackle the challenging link prediction problem,
aiming at figuring out whether some pairs of nodes from a graph are connected
by unobserved edges. However, these models focus on undirected graphs and
therefore ignore the potential direction of the link, which is limiting for
numerous real-life applications. In this paper, we extend the graph AE and VAE
frameworks to address link prediction in directed graphs. We present a new
gravity-inspired decoder scheme that can effectively reconstruct directed
graphs from a node embedding. We empirically evaluate our method on three
different directed link prediction tasks, for which standard graph AE and VAE
perform poorly. We achieve competitive results on three real-world graphs,
outperforming several popular baselines.Comment: ACM International Conference on Information and Knowledge Management
(CIKM 2019
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
On the Consistency of Average Embeddings for Item Recommendation
A prevalent practice in recommender systems consists in averaging item
embeddings to represent users or higher-level concepts in the same embedding
space. This paper investigates the relevance of such a practice. For this
purpose, we propose an expected precision score, designed to measure the
consistency of an average embedding relative to the items used for its
construction. We subsequently analyze the mathematical expression of this score
in a theoretical setting with specific assumptions, as well as its empirical
behavior on real-world data from music streaming services. Our results
emphasize that real-world averages are less consistent for recommendation,
which paves the way for future research to better align real-world embeddings
with assumptions from our theoretical setting.Comment: 17th ACM Conference on Recommender Systems (RecSys 2023
Flow Moods: Recommending Music by Moods on Deezer
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, Flow used to ignore the moods of users when providing recommendations.
In this paper, we present Flow Moods, an improved version of Flow that
addresses this limitation. Flow Moods leverages collaborative filtering, audio
content analysis, and mood annotations from professional music curators to
generate personalized mood-specific playlists at scale. We detail the
motivations, the development, and the deployment of this system on Deezer.
Since its release in 2021, Flow Moods has been recommending music by moods to
millions of users every day.Comment: 16th ACM Conference on Recommender Systems (RecSys 2022) - Industry
pape
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