Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Haoyue Ping"'
Publikováno v:
Proceedings of the VLDB Endowment. 13:1134-1146
Preference analysis is widely applied in various domains such as social choice and e-commerce. A recently proposed framework augments the relational database with a preference relation that represents uncertain preferences in the form of statistical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f7eb7812eeb9cdb14409beb64481fef6
https://doi.org/10.14778/3384345.3384359
https://doi.org/10.14778/3384345.3384359
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030112370
BiDU@VLDB
BiDU@VLDB
We describe customized synthetic datasets for publishing mobility data. Companies are providing new transportation modalities, and their data is of high value for integrative transportation research, policy enforcement, and public accountability. How
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::108e8a68cec106a737c5e78909525b36
https://doi.org/10.1007/978-3-030-11238-7_2
https://doi.org/10.1007/978-3-030-11238-7_2
Publikováno v:
SIGMOD Conference
Models of uncertain preferences, such as Mallows, have been extensively studied due to their plethora of application domains. In a recent work, a conceptual and theoretical framework has been proposed for supporting uncertain preferences as first-cla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::06e313b2f6af9553a1a367af94731bf0
https://doi.org/10.1145/3183713.3196923
https://doi.org/10.1145/3183713.3196923
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 32
Distributions over rankings are used to model user preferences in various settings including political elections and electronic commerce. The Repeated Insertion Model (RIM) gives rise to various known probability distributions over rankings, in parti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4dbb1b55de6e1660f8dc247769dfd76e
https://doi.org/10.1609/aaai.v32i1.11541
https://doi.org/10.1609/aaai.v32i1.11541
Publikováno v:
SSDBM
To facilitate collaboration over sensitive data, we present DataSynthesizer, a tool that takes a sensitive dataset as input and generates a structurally and statistically similar synthetic dataset with strong privacy guarantees. The data owners need
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52885d3964ee77492360d0dfccef800a
https://doi.org/10.1145/3085504.3091117
https://doi.org/10.1145/3085504.3091117
Publikováno v:
PODS
We propose a novel framework wherein probabilistic preferences can be naturally represented and analyzed in a probabilistic relational database. The framework augments the relational schema with a special type of a relation symbol---a preference symb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4c22b4df6a5019e6689a58d07130ca0d
https://doi.org/10.1145/3034786.3056111
https://doi.org/10.1145/3034786.3056111
Publikováno v:
WebDB
In this paper we present a framework for learning mixtures of Mallows models from large samples of incomplete preferences. The problem we address is of significant practical importance in social choice, recommender systems, and other domains where it
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae85d01d933bdae44995a5f5b4a7fda0
https://doi.org/10.1145/2932194.2932202
https://doi.org/10.1145/2932194.2932202