A 2020 perspective on “A generalized stereotype learning approach and its instantiation in trust modeling”

Name: A 2020 perspective on “A generalized stereotype learning approach and its instantiation in trust modeling”
Author: Fang, H., Zhang, J., Şensoy, Murat

İsim	A 2020 perspective on “A generalized stereotype learning approach and its instantiation in trust modeling”
Yazar	Fang, H., Zhang, J., Şensoy, Murat
Basım Tarihi:	2020-03
Basım Yeri	- Elsevier
Konu	Data management, Few-shot learning, Learning with limited data, Recommender systems, User modeling, User profiling, User profiling
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1567-4223
Kayıt Numarası	e100011e-87d4-4fc9-b135-0a71370fe95f
Lokasyon	Computer Science
Tarih	2020-03
Notlar	National Natural Science Foundation of China (NSFC)
Örnek Metin	Owing to the rapid increase of user data and development of machine learning techniques, user modeling has been explored in depth and exploited by both academia and industry. It has prominent impacts in e-commercerelated applications by facilitating users' experience in online platforms and supporting business organizations' decision-making. Among all the techniques and applications, user profiling and recommender systems are two representative and effective ones, which have also obtained growing attention. In view of its wide applications, researchers and practitioners should improve user modeling from two perspectives: (1) more effort should be devoted to obtain more user data via techniques like sensing devices and develop more effective ways to manage complex data; and (2) improving the ability of learning from a limited number of data samples (e.g., few-shot learning) has become an increasingly hot topic for researchers.
DOI	10.1016/j.elerap.2020.100955
Cilt	40

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A 2020 perspective on “A generalized stereotype learning approach and its instantiation in trust modeling”

Yazar Fang, H., Zhang, J., Şensoy, Murat

Basım Tarihi 2020-03

Basım Yeri - Elsevier

Konu Data management, Few-shot learning, Learning with limited data, Recommender systems, User modeling, User profiling, User profiling

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1567-4223

Kayıt Numarası e100011e-87d4-4fc9-b135-0a71370fe95f

Lokasyon Computer Science

Tarih 2020-03

Notlar National Natural Science Foundation of China (NSFC)

Örnek Metin Owing to the rapid increase of user data and development of machine learning techniques, user modeling has been explored in depth and exploited by both academia and industry. It has prominent impacts in e-commercerelated applications by facilitating users' experience in online platforms and supporting business organizations' decision-making. Among all the techniques and applications, user profiling and recommender systems are two representative and effective ones, which have also obtained growing attention. In view of its wide applications, researchers and practitioners should improve user modeling from two perspectives: (1) more effort should be devoted to obtain more user data via techniques like sensing devices and develop more effective ways to manage complex data; and (2) improving the ability of learning from a limited number of data samples (e.g., few-shot learning) has become an increasingly hot topic for researchers.

DOI 10.1016/j.elerap.2020.100955

Cilt 40