Article
Computer Science, Artificial Intelligence
Yufeng Wang, Weidong Zhang, Jianhua Ma, Qun Jin
Summary: This study proposes new clustering MAB-based online recommendation methods, ADCB and ADCB+, which address the insufficient feedbacks and dynamics of individual arrival and item popularity in online recommender systems. The experiments consistently show that these two methods outperform existing dynamic clustering-based online recommendation methods in terms of cumulative reward over recommendation rounds and average Click-Through-Rate.
NEURAL PROCESSING LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Pablo Perez-Nunez, Jorge Diez, Beatriz Remeseiro, Oscar Luaces, Antonio Bahamonde
Summary: This paper proposes a clustering-based method to create a visual summary in the context of a restaurant recommender system. The method includes encoding the photos taken by users who visited the restaurants (items) in a given city using a deep neural network. The resulting visual summary captures the essence of user tastes and illustrates the gastronomic offer of the city. The proposed similarity measure and evaluation method demonstrate the adequacy of the approach for constructing these summaries.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yunfan Li, Mouxing Yang, Dezhong Peng, Taihao Li, Jiantao Huang, Xi Peng
Summary: This paper proposes an online clustering method called twin contrastive learning (TCL), which performs instance-level and cluster-level contrastive learning. It utilizes the rows and columns of the feature matrix to represent instances and clusters, and improves the clustering performance by constructing positive and negative pairs and selecting pseudo-labels based on confidence.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2022)
Article
Computer Science, Artificial Intelligence
Bogdan Walek, Petr Fajmon
Summary: This article proposes a hybrid recommender system that combines collaborative filtering, content-based approaches, and a fuzzy expert system. By analyzing user preferences and activity, and using the fuzzy expert system to create a recommended product list, this system achieves promising results based on standard metrics, even outperforming traditional approaches.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
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
Hao Fan, Kaijun Wu, Hamid Parvin, Akram Beigi, Kim-Hung Pho
Summary: Recommender Systems play a crucial role in addressing the challenges in the field of E-Commerce. Recent Hybrid Recommender Systems combine the strengths of traditional methods and address issues such as cold start, scalability, and sparsity.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2021)
Article
Computer Science, Artificial Intelligence
Douglas Zanatta Ulian, Joao Luiz Becker, Carla Bonato Marcolin, Eusebio Scornavacca
Summary: Research shows that different clustering methods have a significant impact on news recommendation results, with traditional hierarchical methods outperforming optimization methods in performance improvements. Furthermore, parameters may interact with each other, and analyzing them separately could lead to misinterpretation.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Green & Sustainable Science & Technology
Ovidiu Deaconu, Adrian Marius Deaconu, Gabriela Cristina Chitonu, Daniel Taus
Summary: This article discusses the impact of the COVID-19 pandemic on the university education sector, particularly from 2021 onwards. The study found that online teaching has practical and motivational effects on real-world education. The article also introduces a database system that generates representative samples from the university population through testing, and provides empirical research in specific courses.
Article
Computer Science, Cybernetics
Dina Nawara, Rasha Kashef
Summary: This article proposes a multi-CARS based on consensus clustering (MCARS-CC) to address the challenges of data sparsity, real-time scalability, and personalization in existing recommendation systems. Experimental results show that the proposed model outperforms other baseline techniques in terms of accuracy and error-based metrics, and incorporating hypergraph partitioning algorithm (HGPA) further improves the performance of the model.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
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, Artificial Intelligence
Wenhui Liao, Qian Zhang, Bo Yuan, Guangquan Zhang, Jie Lu
Summary: This article proposes a novel multidomain recommender system called HMRec to address the challenges of exploiting valuable information from multiple source domains and ensuring positive transfer from heterogeneous data. By extracting domain-shared and domain-specific features, HMRec enables knowledge transfer between multiple heterogeneous source and target domains. Extensive experiments demonstrate that HMRec effectively increases rating prediction accuracy in the target domain and outperforms six state-of-the-art baselines.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Imen Ben Sassi, Sadok Ben Yahia, Innar Liiv
Summary: This study focuses on integrating context-awareness and multi-criteria decision making to address the challenges faced by music recommender systems. The introduction of a new multi-criteria recommendation approach, MORec, applies aggregation techniques to understand the relationship between context and overall ratings, and uses the K-means algorithm to generate predictive models.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Mansoureh Ghiasabadi Farahani, Javad Akbari Torkestani, Mohsen Rahmani
Summary: Personalized recommender systems provide preferred services based on user preferences and interests. This study proposes a framework using learning automata to create adaptive user profiling for a personalized recommender system, addressing research gaps and the cold start problem. Experimental results on movie datasets demonstrate that the proposed algorithm outperforms existing approaches in terms of precision, recall, MAE, and RMSE.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos
Summary: Recommender systems are designed to meet end user needs, but in some domains there are multiple stakeholders involved. Collaborative filtering systems often have popularity bias, so considering other stakeholders' preferences can help counter this bias and generate fairer recommendations.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)