scholarly journals Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems

Informatics ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 35 ◽  
Author(s):  
Manuel Pozo ◽  
Raja Chiky ◽  
Farid Meziane ◽  
Elisabeth Métais
Keyword(s):  
Active Learning ◽  
Recommender Systems ◽  
Cold Start ◽  
Learning Method ◽  
Learning Techniques ◽  
Learning Technique ◽  
Active Learning Method ◽  
Public Dataset

This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aims to understand their preferences to the related items. In this paper, we propose an active learning technique that exploits past users’ interests and past users’ predictions in order to identify the best questions to ask. Our technique achieves a better performance in terms of precision (RMSE), which leads to learn the users’ preferences in less questions. The experimentations were carried out in a small and public dataset to prove the applicability for handling cold start issues.

scholarly journals Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems

2018 ◽  
Author(s):  
Manuel Pozo ◽  
Raja Chiky ◽  
Farid Meziane ◽  
Elisabeth Metais
Keyword(s):  
Active Learning ◽  
Recommender Systems ◽  
Cold Start ◽  
Learning Method ◽  
Maximum Information ◽  
Learning Techniques ◽  
Learning Technique ◽  
Active Learning Method

This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aim to understand their preferences to the related items. Example of questions may include "do you like this book?" and the users answer,"yes", "no", "I have not read it (unknown)", will reflect the degree of interest for the item by the users. As a consequence, the system can learn the users' preferences from these answers. The goal of active learning is to correctly choose the questions (items) for users. Thus it is necessary to personalize the questionnaires to retrieve the maximum information by avoiding "unknown" answers. In this paper, we propose an active learning technique that exploits past users' interests and past users' predictions in order to identify the best questions to ask.

Quality-driven Deep Active Learning Method for 3D Brain MRI Segmentation

2021 ◽  
Author(s):  
Zhenxi Zhang ◽  
Jie Li ◽  
Chunna Tian ◽  
Zhusi Zhong ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Active Learning ◽  
Brain Mri ◽  
Learning Method ◽  
Mri Segmentation ◽  
Active Learning Method

scholarly journals THE EFFECT OF ACTIVE LEARNING METHOD ON STUDENTS’ ATTITUDE TOWARDS MATHEMATICS

2020 ◽  
Vol 9 ◽  
Author(s):  
Bibigul Kazmagambet ◽  
Zhansaya Ibraimova ◽  
Serkan Kaymak
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Teaching Strategy ◽  
Learning Method ◽  
Learning Group ◽  
Attitude Towards Mathematics ◽  
Active Learning Method ◽  
The Republic ◽  
Active Learning Strategy ◽  
The Impact

The world is changing so fast, and therefore education needs to adapt to the challenges of times. In order to update the content of school education in the Republic of Kazakhstan modern trends are going to be used. These trends contain pedagogical methods that can be used to preserve and even increase internal motivation, as active learning. Active learning method is an treatment where students participate or interact with the learning process, as opposed to passively taking in the information.The goal of this study is to identify the impact of active learning method on 10th grade students’ attitude towards mathematics of the students the second semester of the school year 2019-2020. More specifically, it attempted to determine and compare the attitude toward mathematics of students’ exposure to active learning and traditional teaching strategy. The Likert scale used to evaluate the attitude of students toward mathematics. Mean, Cronbach  value, T-test were the statistical tools used in anatomizing and interpreting the research data. The discovering showed that the students in the active learning group had auspicious attitude than students in the conventional teaching group. According to the findings after research, we saw the direct relation between attitude and active learning. It is concluded that the students’ attitude toward mathematics was better by using active learning strategy. It is recommended that mathematics teacher should use active learning strategy in order to improve the attitude toward mathematics of the students.Keywords:  attitude, mathematics, active learning

Beyond plagiarism: An active learning method to analyze causes behind code-similarity

2014 ◽  
Vol 70 ◽  
pp. 161-172 ◽  
Author(s):  
Feng-Pu Yang ◽  
Hewijin Christine Jiau ◽  
Kuo-Feng Ssu
Keyword(s):  
Active Learning ◽  
Learning Method ◽  
Active Learning Method
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