Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Lucas Maystre"'
Publikováno v:
Proceedings of the International AAAI Conference on Web and Social Media. 16:663-674
Digital media platforms give users access to enormous amounts of content that they must explore to avoid boredom and satisfy their needs for heterogeneity. Existing strands of work across psychology, marketing, computer science, and music underscore
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b1e5829b69b2d8b6ac1fb4e52d7470b
https://doi.org/10.1609/icwsm.v16i1.19324
https://doi.org/10.1609/icwsm.v16i1.19324
Autor:
Praveen Chandar, Brian St. Thomas, Lucas Maystre, Vijay Pappu, Roberto Sanchis-Ojeda, Tiffany Wu, Ben Carterette, Mounia Lalmas, Tony Jebara
Publikováno v:
Proceedings of the ACM Web Conference 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f231b0e964ac68c73b72ed672ba27c7b
https://doi.org/10.1145/3485447.3512038
https://doi.org/10.1145/3485447.3512038
Publikováno v:
WWW
We consider the problem of predicting users’ preferences on online platforms. We build on recent findings suggesting that users’ preferences change over time, and that helping users expand their horizons is important in ensuring that they stay en
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b7a2c6ed003be77c8d7ed475717a9f52
https://doi.org/10.1145/3442381.3450028
https://doi.org/10.1145/3442381.3450028
Autor:
Mounia Lalmas, Casper Worm Hansen, Brian Brost, Lucas Maystre, Christian Hansen, Rishabh Mehrotra
Publikováno v:
WSDM
Algorithmic recommendations shape music consumption at scale, and understanding the impact of various algorithmic models on how content is consumed is a central question for music streaming platforms. The ability to shift consumption towards less pop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e124fb94b09a2478cfe54fc392f3899
https://doi.org/10.1145/3437963.3441775
https://doi.org/10.1145/3437963.3441775
Publikováno v:
Machine Learning and Knowledge Discovery in Databases. Research Track ISBN: 9783030865191
ECML/PKDD (2)
ECML/PKDD (2)
Variational autoencoders are a versatile class of deep latent variable models. They learn expressive latent representations of high dimensional data. However, the latent variance is not a reliable estimate of how uncertain the model is about a given
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::620dea6f6779a4906f500700afb0ad59
https://doi.org/10.1007/978-3-030-86520-7_6
https://doi.org/10.1007/978-3-030-86520-7_6
Autor:
Rishabh Mehrotra, Mounia Lalmas, Lucas Maystre, Brian Brost, Casper Worm Hansen, Federico Tomasi, Christian Hansen
Publikováno v:
RecSys
Hansen, C, Hansen, C, Maystre, L, Mehrotra, R, Brost, B, Tomasi, F & Lalmas, M 2020, Contextual and Sequential User Embeddings for Large-Scale Music Recommendation . in RecSys 2020 : 14th ACM Conference on Recommender Systems . Association for Computing Machinery, pp. 53-62, 14th ACM Conference on Recommender Systems, RecSys 2020, Virtual, Online, Brazil, 22/09/2020 . https://doi.org/10.1145/3383313.3412248
Hansen, C, Hansen, C, Maystre, L, Mehrotra, R, Brost, B, Tomasi, F & Lalmas, M 2020, Contextual and Sequential User Embeddings for Large-Scale Music Recommendation . in RecSys 2020 : 14th ACM Conference on Recommender Systems . Association for Computing Machinery, pp. 53-62, 14th ACM Conference on Recommender Systems, RecSys 2020, Virtual, Online, Brazil, 22/09/2020 . https://doi.org/10.1145/3383313.3412248
Recommender systems play an important role in providing an engaging experience on online music streaming services. However, the musical domain presents distinctive challenges to recommender systems: tracks are short, listened to multiple times, typic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c383bb226e5003ee79b441e4aa406b88
https://doi.org/10.1145/3383313.3412248
https://doi.org/10.1145/3383313.3412248
Publikováno v:
WWW
On many online platforms, users can engage with millions of pieces of content, which they discover either organically or through algorithmically-generated recommendations. While the short-term benefits of recommender systems are well-known, their lon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40ec0aafa834c3ec8f8ceadf49347689
https://doi.org/10.1145/3366423.3380281
https://doi.org/10.1145/3366423.3380281
Publikováno v:
KDD
Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics. We achieve this by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd6235d0655c4f073b8a756134860b73
http://arxiv.org/abs/1903.07746
http://arxiv.org/abs/1903.07746
Publikováno v:
KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project. Existing solutions either rely on a user reputation sy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b48e6d952f30545641b8bb0f59fa091c
https://doi.org/10.1145/3219819.3219979
https://doi.org/10.1145/3219819.3219979