Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Eliezer de Souza da Silva"'
Publikováno v:
WSDM
In recent years session-based recommendation has emerged as an increasingly applicable type of recommendation. As sessions consist of sequences of events, this type of recommendation is a natural fit for Recurrent Neural Networks (RNNs). Several addi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0be240b6ed72be0cf2c99cf80364b3ef
https://doi.org/10.1145/3289600.3290987
https://doi.org/10.1145/3289600.3290987
Autor:
Eliezer de Souza da Silva
Publikováno v:
WSDM
This project is focused on the design of probabilistic models for recommender systems and collaborative ltering by extending and creating new models to include rich contextual and content information (content, user social network, location, time, use
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0ec5fd51e6d05259d690b4d6a359f3d
https://doi.org/10.1145/3018661.3022751
https://doi.org/10.1145/3018661.3022751
Autor:
Dirk Ahlers, Eliezer de Souza da Silva
Publikováno v:
GIR
New retrieval models promise deeper integration of multiple features and sources of information. The inclusion of thematic and location features in a joint factorization model allows location to be modeled as a first-class feature and can improve a r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abfdcfac3d43250b874dd51881b5097f
https://hdl.handle.net/11250/2496482
https://hdl.handle.net/11250/2496482
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783319712482
ECML/PKDD (1)
ECML/PKDD (1)
We introduce Poisson Matrix Factorization with Content and Social trust information (PoissonMF-CS), a latent variable probabilistic model for recommender systems with the objective of jointly modeling social trust, item content and user’s preferenc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::881f9bfa97dfe4d0010a31c78a7399ab
https://doi.org/10.1007/978-3-319-71249-9_32
https://doi.org/10.1007/978-3-319-71249-9_32
Publikováno v:
Similarity Search and Applications ISBN: 9783319119878
SISAP
SISAP
Locality-Sensitive Hashing (LSH) is extremely competitive for similarity search, but works under the assumption of uniform access cost to the data, and for just a handful of dissimilarities for which locality-sensitive families are available. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c1b641cb135c73ed4aef73aeb511197
https://doi.org/10.1007/978-3-319-11988-5_8
https://doi.org/10.1007/978-3-319-11988-5_8