Article
Computer Science, Artificial Intelligence
Hiroki Morise, Kyohei Atarashi, Satoshi Oyama, Masahito Kurihara
Summary: This study investigates the effectiveness of collaborative filtering methods using deep learning and multicriteria evaluation data in recommendation systems. The experimental results show that the proposed methods with deep learning outperform traditional methods in both recommendation and rating aggregation.
APPLIED SOFT COMPUTING
(2022)
Review
Computer Science, Artificial Intelligence
R. J. Kuo, Shu-Syun Li
Summary: With the rapid development of electronic commerce, the recommender system has emerged to assist users' decision-making processes and enable precision marketing. This study utilized the PSO algorithm to solve the problem of data sparsity, while BERT was applied to extract consumer feedback characteristics. The combination of different data types improved the recommendation performance, as evidenced by outperforming existing methods on Amazon datasets in terms of mean absolute error and mean squared error.
APPLIED SOFT COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Silvana Vanesa Aciar, Ramon Fabregat, Teodor Jove, Gabriela Aciar
Summary: Recommender systems are essential in addressing information overload, providing opinions and experiences that influence user purchasing decisions. This work presents a product recommender system based on collaborative filtering, filtering reviews and offering necessary answers to assist users.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Anchen Li, Xueyan Liu, Bo Yang
Summary: This paper introduces a model that improves recommendations by harnessing additional similarities, constructing user and item relational graphs, and integrating these graphs into the recommendation process using a dual graph neural network. Experimental results show that this approach outperforms several state-of-the-art recommenders.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Razieh Davashi
Summary: In this paper, a fast method called ITUFP is proposed for interactive mining of Top-K UFPs. The method efficiently stores and extracts pattern information by creating UP-Lists and IMCUP-Lists, and only updates the IMCUP-Lists when the K value changes. Experimental results demonstrate that the proposed method is very efficient for interactive mining of Top-K UFPs.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jin Li, Yu Tian, Runze Li, Tianshu Zhou, Jun Li, Kefeng Ding, Jingsong Li
Summary: The study introduces a multicenter hybrid semi-supervised transfer learning model which improves the performance of predictive models by aligning features in standardized representation of multicenter data. Experimental results demonstrate that the model outperforms baseline models with limited patient data resources and exhibits good model generalizability.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2021)
Article
Computer Science, Information Systems
Md Mostafizer Rahman, Yutaka Watanobe, Taku Matsumoto, Rage Uday Kiran, Keita Nakamura
Summary: This research proposes an educational data mining framework to support programming learning using unsupervised algorithms. By collecting and preprocessing problem-solving data from an online judge system, and applying MK-means clustering algorithm and frequent pattern growth algorithm for data mining, this framework effectively extracts useful features, patterns, and rules, providing suggestions for programming learning.
Article
Computer Science, Information Systems
Houssem Ben Lahmar, Melanie Herschel
Summary: This paper extends the EVLIN system by combining content-based recommendations with collaborative filtering recommendations to improve the effectiveness of recommendation-based visual data exploration. The recommendations rely on evolution provenance that tracks users' interactions during exploration. Experimental evaluation shows that using both content-based and collaborative-filtering recommendations enables effective interactive visual data exploration.
INFORMATION SYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Meiling Guo, Chao Bian, Lingcheng Meng, Yan Wang
Summary: This work presents tips for doctors to discover semantic similarities and simulations in diagnostic tasks, along with proposing an analytical framework for effective biomedical language and writing. It also discusses the challenges and potential research areas in the rapidly expanding field of big data analysis in healthcare.
MICROPROCESSORS AND MICROSYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Rosa Aguilar, Luis Calisto, Johannes Flacke, Aulia Akbar, Karin Pfeffer
Summary: The use of maptables in collaborative spatial planning processes has proven benefits, but software applications specifically designed for this device are scarce. The development of OGITO, an open-source software application, combined human-centered design and Agile software development principles to continually evolve and meet user expectations through iterative development and feedback. A case study in Sumatra, Indonesia, showed high user satisfaction with OGITO's usability, highlighting the value of iterative development and user feedback for improving tool functionality.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Sahar Ajmal, Muhammad Awais, Khaldoon S. Khurshid, Muhammad Shoaib, Anas Abdelrahman
Summary: Social media is widely used and generates a large amount of data, which poses challenges in terms of storage and processing. This study conducts a systematic literature review to explore data mining-based recommendation systems on social networks from 2011 to 2021. The findings indicate a need for hybrid models that combine different algorithms and methods to improve recommendations on social media using data mining techniques.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Information Systems
Gopal Behera, Neeta Nain
Summary: The tremendous growth in information has led to overwhelming problems in accessing personalized products. To address these issues, we propose an efficient deep collaborative recommender system that embeds item metadata. This system utilizes neural networks and matrix factorization to handle the nonlinearity and sparsity of the data.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Mathematics
Xiang Ying, Keke Zhao, Zhiqiang Liu, Jie Gao, Dongxiao He, Xuewei Li, Wei Xiong
Summary: In this paper, a new wind speed prediction method based on collaborative filtering and the virtual edge expansion graph structure is proposed, which can effectively learn and utilize the spatial correlation of wind speed sequences, and shows superior predictive performance compared to traditional methods.
Article
Computer Science, Hardware & Architecture
Chunfeng Hou, Junjing Zhang, Jian Wang
Summary: The text discusses the management of chronic obstructive pulmonary disease and the importance of using remote monitoring technology and smartphone applications to improve disease management. It also emphasizes the classification of medical data, privacy protection, and the application of big data technology in the medical field.
MICROPROCESSORS AND MICROSYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Xiaoning Liu, Yifeng Zheng, Xun Yi, Surya Nepal
Summary: Medical time series data analytics based on dynamic time warping (DTW) is beneficial for modern medical research. However, collaboration among multiple healthcare institutions and privacy protection are challenges. This study presents a novel system that combines cryptography and data mining techniques to enable privacy-preserving DTW-based analytics on distributed medical time series datasets.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)