Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Heidar Davoudi"'
Publikováno v:
IEEE Transactions on Affective Computing. :1-18
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c01e5b6d7dadfb73ffeb5fa548c9f40f
https://doi.org/10.1109/taffc.2023.3270115
https://doi.org/10.1109/taffc.2023.3270115
Publikováno v:
World Scientific Annual Review of Artificial Intelligence.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f3ce1c7c85d1b221eec5f220ada8284
https://doi.org/10.1142/s2811032323500017
https://doi.org/10.1142/s2811032323500017
Publikováno v:
Journal of Business Research. 135:112-126
Understanding the high likelihood of a dissatisfied customer leaving, customer satisfaction modeling has received significant attention by marketers and academic research. The major challenge in customer satisfaction modeling is the low response rate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7515fc5a69835879481c801ce7a1fd9e
https://doi.org/10.1016/j.jbusres.2021.06.006
https://doi.org/10.1016/j.jbusres.2021.06.006
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 33:3394-3409
Subscription-based online newspapers usually offer non-subscribed users a certain number of free articles in a period of time, and then directs them to a page (called paywall) asking for subscription. This approach (also known as metered or fixed pay
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5ca8ccc9ddfa335a2e316a4024155fd8
https://doi.org/10.1109/tkde.2020.2969419
https://doi.org/10.1109/tkde.2020.2969419
Autor:
Steve Bakos, Heidar Davoudi
Publikováno v:
2022 IEEE Conference on Games (CoG).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27aa05888b04c5813274ed0d8cdd475a
https://doi.org/10.1109/cog51982.2022.9893546
https://doi.org/10.1109/cog51982.2022.9893546
Autor:
Jaroslaw Szlichta, Piotr Mierzejewski, Spencer Bryson, Morteza Zihayat, Yuliya Lytvyn, Mehdi Kargar, Lukasz Golab, Heidar Davoudi
Publikováno v:
Information Retrieval Journal. 23:502-524
There is a growing need to explore attributed graphs such as social networks, expert networks, and biological networks. A well-known mechanism for non-technical users to explore such graphs is keyword search, which receives a set of query keywords an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15956c7248d5e0cf82e230076e350af5
https://doi.org/10.1007/s10791-020-09379-9
https://doi.org/10.1007/s10791-020-09379-9
Publikováno v:
Decision Support Systems. 117:14-27
News platforms exhibit both the challenges as well as opportunities for enhancing the functionalities of recommendation systems in today's big data environment. Novel use of big data storage and programming models can improve news recommendation syst
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c967a08f36ea251d3edb1b54ff754fc9
https://doi.org/10.1016/j.dss.2018.12.001
https://doi.org/10.1016/j.dss.2018.12.001
Publikováno v:
IRI
Recent advances in natural language processing have shown the effectiveness of statistical and neural networkbased algorithms in a deep understanding of textual data. We demonstrate that the result of NLP analysis on text documents can enrich relatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6cffd695392614756f8e1c93ce70762e
https://doi.org/10.1109/iri49571.2020.00064
https://doi.org/10.1109/iri49571.2020.00064
Publikováno v:
Advances in Artificial Intelligence ISBN: 9783030473570
Canadian Conference on AI
Canadian Conference on AI
The problem of automatic question generation from text is of increasing importance due to many useful applications. While deep neural networks achieved success in generating questions from text paragraphs, they mainly focused on a whole paragraph in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5dfb5c93ac160e321767f5fbab568746
https://doi.org/10.1007/978-3-030-47358-7_40
https://doi.org/10.1007/978-3-030-47358-7_40
Publikováno v:
COLING
Automatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection remains a challenging task, due to a la
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc688b48037b5eac412a36cbbe30c303
https://doi.org/10.18653/v1/2020.coling-main.20
https://doi.org/10.18653/v1/2020.coling-main.20