Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Azin Ghazimatin"'
Publikováno v:
2022 ACM Conference on Fairness, Accountability, and Transparency.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c74f0f73194e6063adcd908bd151549
https://doi.org/10.1145/3531146.3534641
https://doi.org/10.1145/3531146.3534641
Publikováno v:
SIGIR '21
Understanding why specific items are recommended to users can significantly increase their trust and satisfaction in the system. While neural recommenders have become the state-of-the-art in recent years, the complexity of deep models still makes the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa3fc029d016f2abf725e001c726ee77
Publikováno v:
The Web Conference 2021
WWW
WWW
System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explana
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4e99c3ff4471da44c3c62920c1e113d
https://hdl.handle.net/21.11116/0000-0008-0303-121.11116/0000-0009-10A8-7
https://hdl.handle.net/21.11116/0000-0008-0303-121.11116/0000-0009-10A8-7
Autor:
Azin Ghazimatin
Publikováno v:
SIGIR
Users are increasingly relying on personalized recommendations (such as news, songs, products) for their daily information consumption. To deliver personalized content to users, Heterogeneous Information Network (HIN)-based recommender systems integr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f41a94daca6e8f0e93f4c599bed366ee
https://doi.org/10.1145/3397271.3401451
https://doi.org/10.1145/3397271.3401451
Publikováno v:
WSDM 2020-13th ACM International Conference on Web Search and Data Mining
WSDM 2020-13th ACM International Conference on Web Search and Data Mining, Feb 2020, Houston, Texas, United States
WSDM
HAL
WSDM '20
WSDM 2020-13th ACM International Conference on Web Search and Data Mining, Feb 2020, Houston, Texas, United States
WSDM
HAL
WSDM '20
Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4b5c9c2d87b3c1a97bca7e88449fb78
http://arxiv.org/abs/1911.08378
http://arxiv.org/abs/1911.08378
{FAIRY}: {A} Framework for Understanding Relationships between Users' Actions and their Social Feeds
Publikováno v:
WSDM '19
Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the plat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::501eae6e91c0acd2710b050fd8031c88
https://hdl.handle.net/21.11116/0000-0005-83B9-621.11116/0000-0005-83BB-4
https://hdl.handle.net/21.11116/0000-0005-83B9-621.11116/0000-0005-83BB-4
Publikováno v:
CIKM
CIKM'17
CIKM'17
Search engines in online communities such as Twitter or Facebook not only return matching posts, but also provide links to the profiles of the authors. Thus, when a user appears in the top-k results for a sensitive keyword query, she becomes widely e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::043d286e38b83a0789375e61a9c7a777
https://doi.org/10.1145/3132847.3133040
https://doi.org/10.1145/3132847.3133040
Publikováno v:
Journal of Combinatorial Optimization. 31:743-757
Given a random variable $O \in \mathbb{R}$ and a set of experts $E$, we describe a method for finding a subset of experts $S \subseteq E$ whose aggregated opinion best predicts the outcome of $O$. Therefore, the problem can be regarded as a team form
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c659e3394811128fd437503199741ae
https://doi.org/10.1007/s10878-014-9784-3
https://doi.org/10.1007/s10878-014-9784-3