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
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
Computer Science, Artificial Intelligence
Ravi Nahta, Yogesh Kumar Meena, Dinesh Gopalani, Ganpat Singh Chauhan
Summary: Generative models encompass latent variable generative models and autoregressive models. Autoregressive models have tractable density without latent variables and are used to learn joint distribution over input variables. However, there is limited research on NADE models in the recommendation literature, and they have limitations such as inability to perform abstraction and slow sampling rate.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
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
Computer Science, Artificial Intelligence
Jesus Bobadilla, Fernando Ortega, Abraham Gutierrez, Angel Gonzalez-Prieto
Summary: The research introduces a method to incorporate stochasticity into deep learning models using variational autoencoders, aiming to improve the performance of recommender systems. By introducing variational techniques in the latent space, this approach can be applied as a plugin to current and future models, demonstrating superior performance in experiments.
NEURAL COMPUTING & APPLICATIONS
(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)
Article
Computer Science, Information Systems
Alvaro Gonzalez, Fernando Ortega, Diego Perez-Lopez, Santiago Alonso
Summary: Recommender Systems, an essential tool in streaming and marketplace systems, have been found to exhibit clear bias and unfairness towards minorities and underrepresented groups. This paper analyzes the demographic characteristics of a gold standard dataset and proposes Soft Matrix Factorization (SoftMF) to balance predictions and reduce existing inequality.
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, Information Systems
Lianghao Xia, Chao Huang, Yong Xu, Huance Xu, Xiang Li, Weiguo Zhang
Summary: As deep learning techniques are applied to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed. However, most existing models are not well equipped to handle missing data. In this paper, we propose a Collaborative Reflection-Augmented Autoencoder Network (CRANet) that can leverage both observed and unobserved user-item interactions to improve recommendation performance. We also introduce a robust joint training algorithm using regularization-based tied-weights. Experimental results show that debiasing negative signals improves the performance compared to state-of-the-art techniques.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2022)
Article
Mathematics, Interdisciplinary Applications
Lei Fu, XiaoMing Ma
Summary: With the popularization of the Internet and the increasing complexity of e-commerce systems, the application of network marketing recommendation systems has greatly improved these issues, although challenges such as data sparsity and user interest drift still exist.
Article
Chemistry, Multidisciplinary
Abebe Tegene, Qiao Liu, Yanglei Gan, Tingting Dai, Habte Leka, Melak Ayenew
Summary: A collaborative recommender system based on a latent factor model has achieved significant success in the field of personalized recommender systems. However, the latent factor model suffers from sparsity problems and limited ability in extracting non-linear data features, resulting in poor recommendation performance. In this paper, we propose a dual deep learning and embedding-based latent factor model that considers dense user and item feature vectors to overcome these problems and improve rating prediction performance.
APPLIED SCIENCES-BASEL
(2023)
Review
Computer Science, Artificial Intelligence
Atena Torkashvand, Seyed Mahdi Jameii, Akram Reza
Summary: This systematic review provides a comprehensive analysis of recent research on deep learning-based collaborative filtering recommender systems. It covers research methodology, paper selection process, method classification, and key information for each selected paper. The study finds that CNN, AE, DNN, and hybrid networks are commonly used neural networks in recommender systems, while Python, MATLAB, and Java are frequently used tools. Movies, products, and music recommendation are the most common applications. The study also highlights key challenges and future research directions.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Fahrettin Horasan
Summary: A hybrid model based on latent semantic indexing (LSI) is proposed for collaborative recommender systems (CRS). Comparisons show that models based on LSI outperform the models based on the commonly used Pearson correlation coefficient (PCC) in terms of prediction accuracy and computational complexity.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Hardware & Architecture
Bushra Alhijawi, Ghazi AL-Naymat
Summary: Recommender systems are increasingly important in helping users find their favorite products. While collaborative filtering has achieved remarkable accuracy, it lacks in terms of novelty, diversity, and coverage. In this study, we propose a novel graph-based collaborative filtering method called Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). Through experiments on benchmark datasets, we demonstrate the superiority of PMLG-RS in generating relevant, novel, and diverse recommendations for users.
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Farimah Houshmand-Nanehkaran, Seyed Mohammadreza Lajevardi, Mahmoud Mahlouji-Bidgholi
Summary: The research aims to provide a list of the best recommendations for active users in less time using the fuzzy-genetic collaborative filtering approach. Experimental results show that this method improves the quality and accuracy of recommendations in the recommender system and addresses the sparsity of data, but it cannot solve the cold-start challenge.
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)