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

Title	A 2020 perspective on “A generalized stereotype learning approach and its instantiation in trust modeling”
Author	Fang, H., Zhang, J., Şensoy, Murat
Publication Date:	2020-03
Publication Place	- Elsevier
Subject	Data management, Few-shot learning, Learning with limited data, Recommender systems, User modeling, User profiling, User profiling
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1567-4223
Record ID	e100011e-87d4-4fc9-b135-0a71370fe95f
Library Location	Computer Science
Date	2020-03
Notes	National Natural Science Foundation of China (NSFC)
Sample Text	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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Özyeğin University

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

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

Publication Date 2020-03

Publication Place - Elsevier

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

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1567-4223

Record ID e100011e-87d4-4fc9-b135-0a71370fe95f

Library Location Computer Science

Date 2020-03

Notes National Natural Science Foundation of China (NSFC)

Sample Text 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