Article
Computer Science, Artificial Intelligence
Rabeh Ravanifard, Abdolreza Mirzaei, Wray Buntine, Mehran Safayani
Summary: Listwise collaborative filtering algorithms are gaining interest for their efficiency and accuracy in recommendation systems. A Bayesian graphical model called CALCF is proposed in this work to integrate text information into listwise CF, achieving better performance in recommendation tasks.
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
Pham Minh Thu Do, Thi Thanh Sang Nguyen
Summary: This paper proposes a novel semantic-enhanced Neural Collaborative Filtering (NCF) model for movie rating prediction and recommendation tasks. By building a semantic knowledge base and user behavior analytic model, combined with user preferences and recommendation model, the proposed model shows better recommendation performance in experiments.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Sumaia Al-Ghuribi, Shahrul Azman Mohd Noah, Mawal Mohammed
Summary: Collaborative filtering (CF) approaches generate user recommendations based on user similarities, but sparse or unavailable user ratings can be supplemented with implicit ratings derived from user reviews using sentiment analysis. This study proposes four methods to calculate implicit ratings by incorporating sentiment degrees and aspect-sentiment word pairs, and evaluates their effectiveness in enhancing CF performance.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Yassine Afoudi, Mohamed Lazaar, Mohammed Al Achhab
Summary: Recommendation systems are tools that provide information based on user preferences and behavior, utilizing methods like Collaborative Filtering, Content Based Approach, and neural network techniques. Research shows that a hybrid recommender framework method improves accuracy and efficiency compared to traditional Collaborative Filtering methods.
SIMULATION MODELLING PRACTICE AND THEORY
(2021)
Review
Computer Science, Information Systems
Fethi Fkih
Summary: This paper provides an in-depth review of similarity measures used in collaborative filtering-based recommender systems. Through experimental studies, the performance of different measures is compared, and important conclusions are drawn. Evaluation results show that different similarity measures have different suitability in user-based and item-based recommendations.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
F. Ortega, J. Mayor, D. Lopez-Fernandez, R. Lara-Cabrera
Summary: CF4J 2.0 is a framework designed for research experiments based on collaborative filtering, with features like implemented algorithms, quality measures, parallel execution, and abstract classes for developers to customize. The new version focuses on simple deployment, reproducible science, hyper-parameter optimization, data analysis, and community openness as an open-source project.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Cybernetics
Kang Liu, Feng Xue, Shuaiyang Li, Sheng Sang, Richang Hong
Summary: Multimedia-based recommendation is a challenging task that aims to explore multimodal user preference cues and provide personalized recommendations. However, current solutions are limited by multimodal noise contamination. To address this issue, researchers propose a hierarchical framework to separately learn collaborative signals and multimodal preference cues, and take measures to alleviate noise contamination.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Peng Zhang, Baoxi Liu, Tun Lu, Hansu Gu, Xianghua Ding, Ning Gu
Summary: The development of ICT and Web 2.0 has led to the emergence of diverse social ecosystems. User-generated textual content is the most important type of content in these ecosystems, but current modeling methods have limitations. Therefore, we propose a new model that can accurately model user-generated textual content in social ecosystems.
Review
Computer Science, Artificial Intelligence
Charinya Wangwatcharakul, Sartra Wongthanavasu
Summary: Collaborative filtering, while widely used, has limitations in tracking temporal user preferences and sparse data. The proposed MTUPD system addresses these issues by incorporating multiple transitions in user preference drift and text reviews to improve recommendation accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Fatemeh Rezaimehr, Chitra Dadkhah
Summary: With the increasing amount of data, the use of recommender systems has increased, emphasizing the importance of recommendation quality for users. Most studies focus on collaborative filtering recommender systems, categorizing attack detection methods into clustering, classifying, feature extraction, and probabilistic approaches.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Hardware & Architecture
Qusai Y. Shambour, Abdelrahman H. Hussein, Qasem M. Kharma, Mosleh M. Abualhaj
Summary: Requirements engineering is a critical process in software development that greatly impacts the success of a project. This study proposes an effective hybrid content-based collaborative filtering approach to recommend related requirements from software repositories, thereby mitigating the risk of missing requirements during requirements elicitation.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Aaron Ling Chi Yi, Dae-Ki Kang
Summary: This paper explores the issue of generating location recommendations by considering user social influence and local expert knowledge, proposing a new collaborative filtering framework called FANA-CF. It has been validated through experiments using real-world datasets, showing slight outperformance compared to traditional collaborative filtering methods and personalized mean approaches.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Fahrettin Horasan, Ahmet Hasim Yurttakal, Selcuk Gunduz
Summary: Collaborative filtering is a technique that considers the common characteristics of users and items in recommendation systems. Matrix decompositions, such as SVD and NMF, are widely used in collaborative filtering. In this study, a technique called T-ULVD was used to improve the accuracy and quality of recommendations. Experimental results showed that T-ULVD achieved better results compared to NMF and performed as well as or even better than SVD. This study may provide guidance for future research on solving the cold-start problem and reducing sparsity in collaborative filtering based recommender systems.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Le Nguyen Hoai Nam
Summary: This paper focuses on the rating prediction phase in memory-based collaborative filtering and improves the prediction accuracy by optimizing an objective function. Experimental results demonstrate that the proposed method outperforms others, especially when the number of selected neighbors is small to medium.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)