Article
Computer Science, Artificial Intelligence
Junrui Liu, Zhen Yang, Tong Li, Di Wu, Ruiyi Wang
Summary: This paper proposes a novel personalized recommendation method called similarity pairwise ranking (SPR) to address the issue of imbalanced data distribution affecting the effectiveness of Bayesian personalized ranking (BPR). By eliminating the score differences between popular and personalized items based on their similarity, SPR enhances the recommendation quality and better meets the individual needs of users. Experimental results demonstrate the superiority of SPR over recent state-of-the-art methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yakun Li, Jiadong Ren, Jiaomin Liu, Yixin Chang
Summary: The study proposes a deep sparse autoencoder model based on adversarial learning to improve rating prediction accuracy in cross-domain recommender systems. By integrating and aligning user and item latent factor spaces, as well as introducing domain factor adaptation algorithm and regularization constraints, the model effectively alleviates data sparsity issues.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Narges Heidari, Parham Moradi, Abbas Koochari
Summary: This paper proposes an attention-based deep learning recommender system, called ADLRS, to address the cold-start and sparsity issues in recommender systems. The method utilizes additional information sources, such as user/item profiles or user reviews, and employs a language model to embed important features. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches and effectively deals with sparsity, cold start, and scalability problems.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yongjie Du, Deyun Zhou, Yu Xie, Jiao Shi, Maoguo Gong
Summary: A user-level distributed matrix factorization framework was proposed to protect privacy using federated learning, enhanced with Homomorphic Encryption and randomized response for stronger privacy protection. Extensive experiments show that this method provides more secure privacy protection with less performance degradation and smaller computational burden.
APPLIED SOFT COMPUTING
(2021)
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, Information Systems
Mario Casillo, Brij B. Gupta, Marco Lombardi, Angelo Lorusso, Domenico Santaniello, Carmine Valentino
Summary: In the world of Big Data, a tool capable of filtering and providing choice support is crucial. This paper proposes a Context-Aware Recommender System based on embedded context, which has been tested on multiple datasets to evaluate its accuracy and achieves promising results.
Article
Computer Science, Information Systems
Pablo Perez-Nunez, Jorge Diez, Oscar Luaces, Antonio Bahamonde
Summary: Recommender systems are valuable tools for companies to understand customer preferences and offer personalized marketing campaigns. Clustering tools are key in detecting groups of customers with similar tastes.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Review
Computer Science, Artificial Intelligence
Zhaoliang Chen, Shiping Wang
Summary: This paper provides an overview of recommender systems based on matrix completion, including the introduction of related algorithms, performance evaluation, and discussion of future research directions.
KNOWLEDGE AND INFORMATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Runzhi Xu, Jianjun Li, Guohui Li, Peng Pan, Quan Zhou, Chaoyang Wang
Summary: The recommender system is crucial in dealing with data explosion, and the application of deep neural networks has become a popular research topic. Methods like SDNN and DualCF have improved the efficiency of capturing user-item relations in recommendation systems, and their effectiveness has been verified through extensive experiments.
INFORMATION SCIENCES
(2022)
Editorial Material
Computer Science, Artificial Intelligence
Yong Zheng, Li Chen, Markus Zanker, Panagiotis Symeonidis
Summary: Recommender systems have successfully alleviated information overload and aided decision making in various domains and applications. This special issue focuses on inviting authors to submit revised and extended versions of their accepted papers on recommender systems, which were presented at the ACM Symposium on Applied Computing in 2020 and 2021. Each submission underwent a rigorous review process to ensure paper quality. The aim is to inspire researchers in the field of recommender systems to go beyond traditional algorithm development and explore new research opportunities.
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Hasan Idhaim, Yousef Kilani, Ayoub Alsarhan, Mohammad Aljaidi, Ala Altaweel, Ahmed Bouridane, Amjad Aldweesh
Summary: This paper introduces the matrix factorization technique in recommender systems and proposes an improved method called NLM+. Experimental results demonstrate that NLM+ significantly improves the recall and precision compared to NLM.
Article
Computer Science, Artificial Intelligence
Ying Zhang, Xiangli Li, Mengxue Jia
Summary: Traditional clustering is an unsupervised learning method, but prior information in actual data can be used for semi-supervised clustering. Pairwise constraints are commonly used prior information that can improve clustering performance. This paper proposes a semi-supervised clustering method that combines pairwise constraints with nonnegative matrix factorization and verifies its effectiveness through experiments.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Jesus Bobadilla, Abraham Gutierrez, Raciel Yera, Luis Martinez
Summary: This paper proposes a method based on Generative Adversarial Networks (GANs) to generate collaborative filtering datasets with specific features. The method uses dense, short, and continuous embeddings for faster and more accurate learning compared to traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Shunmei Meng, Qianmu Li, Lianyong Qi, Xiaolong Xu, Rui Yuan, Xuyun Zhang
Summary: Recommender systems are popular in Internet communities, but traditional collaborative recommendation algorithms may not meet users' security requirements. Deep learning has shown to outperform traditional techniques and can improve user behavior prediction in Recommender systems. An intelligent recommendation method based on multi-interest network and adversarial deep learning is proposed, using multi-source behavior information for better prediction performance and privacy preservation. Extensive experiments show that this method achieves decent prediction performance with security concerns compared to state-of-the-art baselines.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Information Systems
Fernando Ortega, Raul Lara-Cabrera, Angel Gonzalez-Prieto, Jesus Bobadilla
Summary: This paper introduces a new matrix factorization model, BeMF, which provides both prediction values and reliability values. Experimental results show that BeMF outperforms previous baseline methods and models in terms of recommendation quality.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Bin Wu, Xiangnan He, Zhongchuan Sun, Liang Chen, Yangdong Ye
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Article
Computer Science, Information Systems
Zhongchuan Sun, Bin Wu, Youwei Wang, Yangdong Ye
Summary: This paper introduces a sequential graph attention network (SGAT) that utilizes a multiplex directed heterogeneous graph and a vectorization algorithm to address the information understanding problem in next-item recommendation. Experimental results demonstrate the superior performance of SGAT across various datasets.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Qi Zhang, Bin Wu, Zhongchuan Sun, Yangdong Ye
Summary: Sequential recommendation has become popular and essential in various online services. This study proposes a Gating Augmented Capsule Network (GAC) to model personalized item- and factor-level transitions in a fine-grained manner, capturing both item co-occurrence patterns and transitions among items' latent attributes. Extensive experiments demonstrate the effectiveness of GAC compared to state-of-the-art baselines.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Zhongchuan Sun, Bin Wu, Yifan Chen, Yangdong Ye
Summary: This article discusses the methods of modeling sequential behaviors in sequential recommendation. Existing methods typically only use unidirectional past information for recommendations, while future information is also proven to be an important factor. By introducing sequential graphs and manifold translating embedding methods, a bidirectional sequential graph convolutional network is proposed to simultaneously learn from past and future information to better capture the patterns of sequential behaviors. Experimental results verify the superior performance of this method and the benefits of learning from future behaviors.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhongchuan Sun, Bin Wu, Shizhe Hu, Mingming Zhang, Yangdong Ye
Summary: This article proposes a novel framework called AACF and an efficient training strategy to improve GANs in recommender systems. AACF is a differentiable generative adversarial framework that introduces an attention mechanism and virtual items to bridge the gap between the generator and the discriminator. The framework can be stably optimized with gradient descent methods and efficiently trained and scaled up to large datasets.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
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)