Article
Computer Science, Artificial Intelligence
Zeyu Cui, Feng Yu, Shu Wu, Qiang Liu, Liang Wang
Summary: Item representations in recommendation systems are traditionally done using single latent vectors, but utilizing attribute information has recently become popular for better item representations. This article proposes a fine-grained Disentangled Item Representation (DIR) method, representing items as separate attribute vectors for more detailed item information. Experimental results using the LearnDIR strategy show that models developed under DIR framework are effective and efficient, even outperforming state-of-the-art methods in cold-start situations.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Kanika Narang, Yitong Song, Alexander Schwing, Hari Sundaram
Summary: Recommender systems can benefit from a variety of signals influencing user behavior, but existing methods often fail to fully utilize all available information. The 'Fusion Recommender' model is proposed, which models different factors separately and combines them in an interpretable way. This model shows promising results across multiple datasets, outperforming other techniques by over 14% while also providing insights on the importance of each factor.
DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Business
Bernd Heinrich, Marcus Hopf, Daniel Lohninger, Alexander Schiller, Michael Szubartowicz
Summary: The study found that data completeness has a positive impact on the prediction accuracy of recommender systems, while increasing diversity of features does not improve prediction accuracy.
ELECTRONIC MARKETS
(2021)
Article
Computer Science, Information Systems
Bernd Heinrich, Marcus Hopf, Daniel Lohninger, Alexander Schiller, Michael Szubartowicz
Summary: The rapid development of e-commerce has increased competition among providers, making data quality crucial for recommender systems. This paper proposes a data extension procedure that improves recommendation quality, as evidenced by evaluation results using real-world data sets.
INFORMATION SYSTEMS FRONTIERS
(2022)
Article
Computer Science, Information Systems
Yanan Xu, Yanmin Zhu, Jiadi Yu
Summary: Purchase intentions have a significant impact on future purchases, but they are typically complex and subject to change. Empirical study shows that user behaviors of multiple types can indicate intentions, and users may have multiple coexisting category-level intentions that evolve over time. The proposed Intention-Aware Recommender System (TARS) effectively mines complex intentions from diverse user behaviors and outperforms state-of-the-art recommendation methods.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2021)
Article
Computer Science, Information Systems
Le Nguyen Hoai Nam
Summary: To meet the demand for group activities, single-user recommender systems need to be scaled up. This paper introduces the concept of deep profiles and proposes group recommendation methods based on deep profile aggregation. Experiments have shown that group recommendations based on deep profiles are more efficient.
Article
Computer Science, Information Systems
Mehdi Elahi, Danial Khosh Kholgh, Mohammad Sina Kiarostami, Sorush Saghari, Shiva Parsa Rad, Marko Tkalcic
Summary: This paper investigates the impact of different recommender algorithms on popularity bias, comparing user-based and item-based recommendation scenarios to find that recommending items to users can decrease popularity bias. The study also reveals the existence of popularity bias in different recommendation domains and languages.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Computer Science, Cybernetics
Johannes Kunkel, Jurgen Ziegler
Summary: Recommender systems aim to help users in their search and decision making process by selecting a small number of relevant items from a large set of options. However, this may limit users' understanding and exploration of items in their larger context, reducing their perception of transparency and control.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES
(2023)
Article
Mathematics, Interdisciplinary Applications
Jun Ai, Yifang Cai, Zhan Su, Kuan Zhang, Dunlu Peng, Qingkui Chen
Summary: This paper proposes a user-item link prediction algorithm based on resource allocation within the user similarity network to improve prediction accuracy, maintain recommendation diversity, and enhance algorithm scalability by using as few neighbors as possible.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Computer Science, Hardware & Architecture
Nguyen Van Han, Phan Cong Vinh
Summary: Dynamic system is crucial for cognitive science, with cognitive map and fuzzy cognitive map being special cases. Both types of maps have complex state spaces, and linguistic dynamic system is found to be convergent.
MOBILE NETWORKS & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas
Summary: This study uses a multi-granular fuzzy linguistic model to evaluate the different features of health care systems and recommend hospitals based on user preferences. By assessing the opinions of real hospitals, the results of this approach outperform other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Richa, Punam Bedi
Summary: Recommender Systems aid users in finding interesting information, with recent research focusing on incorporating serendipity and novelty to improve user acceptance. A model has been proposed to generate serendipitous recommendations while addressing accuracy and sparsity concerns. Fuzzy inference, cross-domain, and trust-based approaches are utilized for serendipity computation and to enhance recommendation accuracy.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jia Liu, Wei Huang, Tianrui Li, Shenggong Ji, Junbo Zhang
Summary: This paper proposes a multi-domain item-item recommendation method based on cross-domain knowledge graph embedding, which addresses the sparsity and cold start problems faced by traditional recommender systems by analyzing the association between items within the same domain and the interaction between items across diverse domains with the aid of a rich information knowledge graph.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Veronika Bogina, Yuri Variat, Tsvi Kuflik, Eyal Dim
Summary: This study focuses on predicting the next TV program by analyzing the time sequences of TV viewing, aiming to better understand the viewing preferences and habits of TV viewers. The research conducted experiments using various methods, ultimately demonstrating high prediction accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang
Summary: This study focuses on utilizing the indirect influence from high-order neighbors in social networks to enhance the performance of item recommendation. Different from traditional social recommenders, we directly factor social relations in the predictive model to improve user embeddings and recommendation outcomes.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Economics
Ran Fang, Huchang Liao, Zeshui Xu, Enrique Herrera-Viedma
Summary: This paper proposes a method, namely generalized probabilistic linguistic preference relation (GPLPR), to handle comprehensive linguistic preference assessments in multiple forms, and corresponding graph-theory-based approach to improve consistency degree in project management risk assessment.
ECONOMIC RESEARCH-EKONOMSKA ISTRAZIVANJA
(2023)
Article
Computer Science, Artificial Intelligence
Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Enrique Herrera-Viedma
Summary: To manage consensus in opinion dynamics models (ODMs), it is crucial to remove bias from agents' interactions and consider their willingness. We propose a linguistic ODM based on Blockchain technology to build trust and consensus opinion. A Blockchain-enabled trust-building mechanism is used to improve agents' trust and guide them toward consensus opinion.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Feixia Ji, Jian Wu, Francisco Chiclana, Sha Wang, Hamido Fujita, Enrique Herrera-Viedma
Summary: This study proposes an overlapping community-driven feedback mechanism to improve consensus in social network group decision making. By guiding inconsistent subgroups to interact with each other and selecting personalized feedback parameters, this mechanism helps achieve higher levels of consensus.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Mathematics
Yi Zhou, Chonglan Guo, Guo Wei, Enrique Herrera-Viedma
Summary: In this paper, sorted negotiation is introduced into consensus decision making to improve the speed and effectiveness of consensus. The authors construct negotiation models considering efficiency and time, and use an improved genetic algorithm to solve the optimal solution in the context of China's urban demolition negotiation. Assessment criteria for the reasonableness of the sorting sequence are determined by introducing an optimum set of influential individuals.
Article
Computer Science, Information Systems
Julio Herce-Zelaya, Carlos Porcel, Alvaro Tejeda-Lorente, Juan Bernabe-Moreno, Enrique Herrera-Viedma
Summary: Recommender systems help users choose relevant items from a vast selection. The cold start problem, when new items or users are added without previous information, is a major challenge. This article introduces a multi-source dataset optimized for studying and addressing the cold start problem. It also presents a user behavior-driven algorithm using this dataset, which combines collaborative filtering and user-item classification. The results show accurate recommendations and establish the dataset as valuable for future research in recommender systems, particularly regarding the cold start problem.
Article
Engineering, Electrical & Electronic
Qi-Chang An, Hanfu Zhang, Kun Wang, Xinyue Liu, Jianli Wang, Chen Tao, Hong-Wen Li
Summary: In this study, a fiber-linked-wavefront-sensing system was developed to address the issues of restricted optical path spaces and decoherence encountered during the cofocusing and cophasing processes of large-space telescopes. The system achieved high accuracy in both cofocus and cophasing measurements, expanding the application range of astronomical photonics in large-space telescopes.
IEEE PHOTONICS JOURNAL
(2023)
Article
Automation & Control Systems
Jiang Deng, Jianming Zhan, Zeshui Xu, Enrique Herrera-Viedma
Summary: This article proposes a wide three-way decision model on multiscale information systems (MSIS), combining 3WD theory and regret theory, to address the two problems in existing MADM methods. The model effectively tackles misclassification and incorporates decision makers' risk attitudes and psychological behaviors. Experimental analysis confirms the effectiveness, superiority, and stability of the proposed model.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Xiaoxia Xu, Zaiwu Gong, Enrique Herrera-Viedma, Gang Kou, Francisco Javier Cabrerizo
Summary: This article extends the research on uncertain minimum cost consensus models (MCCMs) by incorporating linear uncertainty distributions (LUDs) and considering asymmetric costs. Two novel optimization-based consensus models are proposed, one for obtaining a minimum cost consensus and the other for addressing group decision making problems without presetting a specific consensus level (CL) threshold.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Mahdi Khosravy, Kazuaki Nakamura, Naoko Nitta, Nilanjan Dey, Ruben Gonzalez Crespo, Enrique Herrera-Viedma, Noboru Babaguchi
Summary: Inversion attack (MIA) poses a threat to deep-learning-based recognition systems (DLRSs). This research proposes a social IoT approach for collaborative defense against MIA-generated data clones. The proposed technique utilizes a collaborative recognition system to verify the output of the targeted recognition system, achieving a high detection rate for MIA-generated clones.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Enrique Herrera-Viedma
Summary: This study proposes novel reinforcement learning-based adjustment mechanisms to address the tradeoff between the number of discussion rounds and the harmony degree of decision makers in group decision-making. By converting the decision environment into a Markov decision process, two independent reinforcement learning agents are trained to adjust feedback parameters and weights of decision makers, aiming to reduce discussion rounds and improve harmony degree.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Zaiwu Gong, Xiujuan Ma, Weiwei Guo, Guo Wei, Enrique Herrera-Viedma
Summary: The study proposes a consensus approach with trust relationships and adjustment cost to address the lack of attention to individual decision costs and similarity in expert decision behaviors in the social network decision process. The method consists of three stages: trust propagation, weight allocation, and consensus reaching. Uncertain theory is employed in trust propagation while comprehensive weight allocation is based on network structure and relationship strength. Consensus is considered at both individual and collective group levels using chance-constrained programming models. Comparative analysis is conducted to evaluate the effectiveness and advancement of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Hengjie Zhang, Shenghua Liu, Yucheng Dong, Francisco Chiclana, Enrique Enrique Herrera-Viedma
Summary: This study presents a framework called minimum cost consensus-based failure mode and effect analysis (MCC-FMEA) that considers experts' limited compromise and tolerance behaviors. It introduces two types of behaviors, limited compromise behavior and tolerance behavior, to the MCC-FMEA. The study develops and analyzes a minimum compromise adjustment consensus model and a maximum consensus model with limited compromise behaviors, resulting in an interactive MCC-FMEA framework.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Yejun Xu, Qianqian Wang, Francisco Chiclana, Enrique Herrera-Viedma
Summary: This article presents a new inconsistency identification and modification (IIM) method to improve the consistency of inconsistent fuzzy reciprocal preference relations (FPRs) while retaining the original preference values. The method also addresses the issue of inconsistent FPRs with missing values and provides an estimation approach for the missing preferences. Numerical examples, simulation experiments, and comparisons with existing methods demonstrate the correctness, effectiveness, and robustness of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Chenglong Xu, Chunhui Xu, Wen Xing, Enrique Herrera-Viedma
Summary: This article proposes a novel filtering method with a guided trajectory to address the challenging underwater navigation of autonomous underwater vehicles (AUV). By utilizing the relatively smooth strap-down inertial navigation system (SINS), the proposed algorithm ensures smoothness in motion estimation and maintains local gradient consistency. Simulation results demonstrate high robustness and good smoothness, while real experiments in deep-sea areas further validate the effectiveness and practicability of the method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
