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pro vyhledávání: '"Paudel, Bibek"'
With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident. Previous works have demonstrated that KGs are prone to various social biases, a
Externí odkaz:
http://arxiv.org/abs/2109.10697
Autor:
Paudel, Bibek, Bernstein, Abraham
Publikováno v:
Proceedings of the Web Conference 2021 (WWW '21), April 19--23, 2021, Ljubljana, Slovenia
Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in the
Externí odkaz:
http://arxiv.org/abs/2102.09635
Autor:
Arduini, Mario, Noci, Lorenzo, Pirovano, Federico, Zhang, Ce, Shrestha, Yash Raj, Paudel, Bibek
Publikováno v:
MLG 2020 at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020
Knowledge Graphs (KG) are gaining increasing attention in both academia and industry. Despite their diverse benefits, recent research have identified social and cultural biases embedded in the representations learned from KGs. Such biases can have de
Externí odkaz:
http://arxiv.org/abs/2006.16309
Publikováno v:
BIOKDD 2020 at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020
Augmentation of disease diagnosis and decision-making in healthcare with machine learning algorithms is gaining much impetus in recent years. In particular, in the current epidemiological situation caused by COVID-19 pandemic, swift and accurate pred
Externí odkaz:
http://arxiv.org/abs/2006.16926
Autor:
Paudel, Bibek, Bernstein, Abraham
The suggestions generated by most existing recommender systems are known to suffer from a lack of diversity, and other issues like popularity bias. As a result, they have been observed to promote well-known "blockbuster" items, and to present users w
Externí odkaz:
http://arxiv.org/abs/1909.01495
Autor:
Zhang, Wen, Paudel, Bibek, Wang, Liang, Chen, Jiaoyan, Zhu, Hai, Zhang, Wei, Bernstein, Abraham, Chen, Huajun
Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advan
Externí odkaz:
http://arxiv.org/abs/1903.08948
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select relat
Externí odkaz:
http://arxiv.org/abs/1903.04750
Publikováno v:
CARS-BDA, at the 12th ACM International Conference on Web Search and Data Mining (WSDM), 2019
Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems. This context is especially useful in scenarios where the cost of negative items is high for the users. In this work, we describe
Externí odkaz:
http://arxiv.org/abs/1812.11422
Autor:
Remme, Roy P., Frumkin, Howard, Guerry, Anne D., King, Abby C., Mandle, Lisa, Sarabu, Chethan, Bratman, Gregory N., Giles-Corti, Billie, Hamel, Perrine, Han, Baolong, Hicks, Jennifer L., James, Peter, Lawler, Joshua J., Lindahl, Therese, Liu, Hongxiao, Lu, Yi, Oosterbroek, Bram, Paudel, Bibek, Sallis, James F., Schipperijn, Jasper, Sosič, Rok, de Vries, Sjerp, Wheeler, Benedict W., Wood, Spencer A., Wu, Tong, Daily, Gretchen C.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Jun 01. 118(22), 1-10.
Externí odkaz:
https://www.jstor.org/stable/27040956
Publikováno v:
In Big Data Research 15 July 2021 25