Article
Computer Science, Artificial Intelligence
Yilena Perez-Almaguer, Raciel Yera, Ahmad A. Alzahrani, Luis Martinez
Summary: Group recommender systems recommend items consumed socially by groups, using collaborative filtering as the core algorithm. This study explores a taxonomy for content-based group recommendation systems and analyzes three specific models, as well as proposing a hybrid CB-GRS. Experimentation over well-known datasets is conducted to evaluate the proposals and provide a basis for further research in content-based group recommender systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Mathematics
Sundaresan Bhaskaran, Raja Marappan, Balachandran Santhi
Summary: Different recommendation techniques in e-learning have been designed to provide personalized learning experiences for learners based on their needs and interests. Research shows that a clustering recommender system based on split and conquer strategy has been successful in experiments with different groups and datasets, improving recommendation performance.
Review
Computer Science, Information Systems
Jeevamol Joy, Renumol Vemballiveli Govinda Pillai
Summary: This paper provides insights into recent research trends and technologies in the field of content recommendation in e-learning. Through a literature review, the paper categorizes and analyzes relevant articles on recommendation techniques, data inputs, algorithms, similarity measures, and evaluation metrics.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Communication
Roan Schellingerhout, Davide Beraldo, Maarten Marx
Summary: This article investigates the conditions under which YouTube's recommender system tends to favor conspiracy-classified videos. The study focuses on the personalization and diversified user strategies, rather than non-personalized recommendations and standard watch patterns. Authenticated bots were used to watch YouTube content based on different watch strategies, and the impact on the proportion of conspiracy-classified content recommended was measured. The results show that users exposed to conspiracy-classified content quickly receive more recommendations for similar content, and personalized content input may have a stronger effect.
DIGITAL JOURNALISM
(2023)
Review
Psychology, Educational
Matthew L. Bernacki, Meghan J. Greene, Nikki G. Lobczowski
Summary: Personalized learning involves diverse definitions and implementations, with research spanning various disciplines and designs focusing on learner characteristics and outcomes. Studies often lack prior theoretical conceptualization, yet designs tend to align with existing theories of learning.
EDUCATIONAL PSYCHOLOGY REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
Nabila Amir, Fouzia Jabeen, Zafar Ali, Irfan Ullah, Asim Ullah Jan, Pavlos Kefalas
Summary: This survey fills the gap in the literature by summarizing the strengths, weaknesses, and trends of news recommendation models employing DL methods. It also discusses the commonly used datasets, evaluation methods, and implications for researchers in this area.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Education & Educational Research
Joy Jeevamol, V. G. Renumol
Summary: This paper addresses the new user cold-start problem in e-learning content RSs by proposing an ontology-based content recommender system. By incorporating additional learner data in the recommendation process, the proposed model provides more reliable and personalized recommendations.
EDUCATION AND INFORMATION TECHNOLOGIES
(2021)
Article
Computer Science, Artificial Intelligence
Shengyu Zhang, Fuli Feng, Kun Kuang, Wenqiao Zhang, Zhou Zhao, Hongxia Yang, Tat-Seng Chua, Fei Wu
Summary: The paper investigates the interactions between latent factors in recommender systems and proposes a personalized latent structure learning framework called PlanRec. It personalizes the universally learned dependencies through probabilistic modeling and balances shared knowledge and personalization through uncertainty estimation.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Xiao Liu, Bonan Gao, Basem Suleiman, Han You, Zisu Ma, Yu Liu, Ali Anaissi
Summary: In this article, a novel privacy-aware personalized fitness recommender system is proposed. It introduces a multi-level deep learning framework that learns important features from a large-scale real fitness dataset collected from wearable Internet of Things (IoT) devices to derive intelligent fitness recommendations. Unlike existing approaches, the system infers the fitness characteristics of users from sensory data, minimizing the need for explicitly collecting user identity or biometric information. The proposed models and algorithms predict personalized exercise distance, speed sequence, and heart rate sequence to assist users in achieving fitness goals and guiding future exercises.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Review
Computer Science, Artificial Intelligence
Tieyuan Liu, Qiong Wu, Liang Chang, Tianlong Gu
Summary: This paper provides a systematic review of deep learning-based recommendation systems in e-learning environments. It introduces the concept and classification of recommendation systems, analyzes existing systems, and presents an overall course recommendation system framework. It focuses on the applications of various deep learning techniques and discusses the flaws in current systems and future research opportunities.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Information Systems
Rahul Katarya, Rajat Saini
Summary: The Greedy Clustering Wine Recommender System (GCWRS) utilizes PCA and K-Means clustering algorithms along with a greedy technique to provide personalized and effective wine recommendations, outperforming other standard algorithms. The system tailors recommendations to help users find wines they like based on their individual preferences and needs.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Mansoureh Ghiasabadi Farahani, Javad Akbari Torkestan, Mohsen Rahmani
Summary: Personalized recommender systems rely on accurate and complete user profiles to provide successful recommendation services. To address the changing interests of users, we propose a learning automata-based algorithm that clusters items and adjusts user interests accordingly. Experimental results demonstrate that our algorithm outperforms other approaches in terms of precision, recall, RMSE, and MAE, and shows acceptable performance for new users.
INFORMATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dongjing Wang, Xin Zhang, Dongjin Yu, Guandong Xu, Shuiguang Deng
Summary: The article discusses a personalized music recommender system that incorporates rich content and context data in a unified and adaptive way. By proposing a method called content- and context-aware music embedding (CAME) and integrating deep learning techniques, the system is able to effectively capture the features of music pieces.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Information Systems
Martin Pichl, Eva Zangerle
Summary: As music consumption has shifted towards music streaming platforms in the past decade, users are increasingly relying on music recommender systems to help them discover music they like due to the overwhelming amount of choices available.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Education & Educational Research
Meryem Amane, Karima Aissaoui, Mohammed Berrada
Summary: This paper discusses the main recommendation systems used in E-learning and proposes new research directions to overcome their weaknesses. By including other learners' information in the recommendation process, the cold start problem for new users can be resolved. The objective of the study is to propose an E-learning Recommender System based on Dynamic Ontology and the experiments demonstrate its effectiveness.
EDUCATION AND INFORMATION TECHNOLOGIES
(2022)
Article
Computer Science, Information Systems
Muhammad Rizwan, Aamer Nadeem, Sohail Sarwar, Muddesar Iqbal, Muhammad Safyan, Zia Ul Qayyum
Summary: Software fault prediction is a process that identifies fault-prone modules in order to minimize testing efforts. Existing techniques rely on fault information from previous software versions or clustering with expert opinions. However, these methods may have limitations. In this paper, we propose a comprehensive framework called EkmEx that addresses these limitations and provides significant potential in identifying fault-prone modules.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Engineering, Civil
Farhan Ali, Sohail Sarwar, Qaisar M. Shafi, Muddesar Iqbal, Muhammad Safyan, Zia Ul Qayyum
Summary: Internet of Things (IoTs) is expected to be widely used in logistics and transportation services. One of the major security threats to Maritime Transportation Systems (MTS) is Distributed Denial of Service Attack (DDoS). Therefore, it is crucial to timely and effectively detect such attacks in order to mitigate the potential damages.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Muhammad Kashif, Muddesar Iqbal, Zakir Ullah, Tasos Dagiuklas, Sohail Sarwar, Zia Ul-Qayyum, Muhammad Safyan