Article
Computer Science, Artificial Intelligence
Zhi Liu, Deju Zhang, Chenwei Zhang, Jixin Bian, Junhui Deng, Guojiang Shen, Xiangjie Kong
Summary: Accurate user modeling is crucial for improving user satisfaction with recommended POIs and enriching user experience in point-of-interest (POI) recommendation. However, existing methods lack the ability to capture user-preference knowledge hidden in complex social networks. To address this issue, we propose a novel knowledge-driven and user-aware POI recommendation method called KDRank, which constructs a knowledge graph and employs cross-embedding, knowledge aggregation, and attention mechanism to enhance the accuracy of POI recommendations. Experimental results on real datasets demonstrate the effectiveness of our proposed method.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Wang, Lixin Han, Jingxian Li, Hong Yan
Summary: The tripartite graph model in recommender systems can better handle data sparsity and cold start issues, improving recommendation metrics such as diversity, recall, and precision. Using the Conditional Random Field algorithm, potentially similar users can be identified, uncovering their preferences, and the random walk method further explores users with similar preferences beyond social relationships.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nan Jiang, Li Gao, Fuxian Duan, Jie Wen, Tao Wan, Honglong Chen
Summary: Recommendation systems play a crucial role in the era of social networks, especially in the field of social recommendation. Existing social recommendation methods, though successful, still have room for improvement. A novel attention-based Social Aggregation Neural Networks (SAN) model is proposed to enhance recommendation system performance by simulating global social influence propagation and introducing social attention mechanism.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Lisa Zhang, Zhe Kang, Xiaoxin Sun, Hong Sun, Bangzuo Zhang, Dongbing Pu
Summary: This paper proposes a novel Knowledge-aware representation Graph Convolutional Network for Recommendation (KCRec) framework, which effectively addresses the data sparsity and cold-start problems in collaborative filtering in real recommendation scenarios. By utilizing knowledge graph and user-item relationships, the performance of the recommendation system is improved. Experimental results demonstrate that the proposed method outperforms several state-of-the-art baselines.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Theory & Methods
Zhenyan Ji, Mengdan Wu, Hong Yang, Jose Enrique Armendariz Inigo
Summary: This paper proposes a time-sensitive heterogeneous graph neural network for news recommendation, where one subnet learns the temporal characteristics of user behavior and the other subnet builds an attention-based graph to model user-news-topic associations. Experiments show that the proposed model outperforms state-of-the-art models in accuracy and interpretability.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Yongjing Hao, Jun Ma, Pengpeng Zhao, Guanfeng Liu, Xuefeng Xian, Lei Zhao, Victor S. Sheng
Summary: Graph neural networks (GNNs) have been widely used in recommendation systems due to their advantages in graph representation learning, and many successful models have applied graph-based methods for sequential recommendation. However, existing research only considers the number of interactions between items, neglecting the multi-dimensional transformation relationships. Therefore, we propose a Category and Time information integrated Graph Neural Network (CT-GNN) that combines item category and interaction time information to form fine-grained item representations, and design a temporal self-attention network for dynamic user preference modeling and next-item recommendation. Experimental results on real-world datasets demonstrate the excellent performance of the proposed model. (c) 2023 Elsevier Ltd. All rights reserved.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Lei Chen, Guixiang Zhu, Weichao Liang, Youquan Wang
Summary: Trip recommendation is an intelligent service that offers personalized itinerary plans to tourists in unfamiliar cities, considering temporal and spatial constraints. In this article, we propose MORL-Trip, a Multi-Objective Reinforcement Learning approach, to address the challenges of capturing users' dynamic preferences and enhancing the diversity and popularity of personalized trips. MORL-Trip models the recommendation as a Markov Decision Process and incorporates sequential, geographic, and order information to learn user's context. It also introduces a composite reward function to reinforce accuracy, popularity, and diversity as principal objectives.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xia Xiao, Junyan Xu, Jiaying Huang, Chengde Zhang, Xinzhong Chen
Summary: This paper proposes an innovative approach to address the issue of distinguishing authors' research interests caused by sparse binary interactions. It introduces a ternary coauthor recommendation method that utilizes an academic heterogeneous graph and a multilayer perceptron for enriching author-paper interactions, thus enhancing paper recommendation performance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuzhi Song, Hailiang Ye, Ming Li, Feilong Cao
Summary: This paper introduces a novel deep GNN model MAF-GNN, which improves the quality of latent feature representations for users and items through multi-graph attention fusion and multi-scale latent feature modules. Experimental results show that MAF-GNN outperforms existing methods in recommendation systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Wenting Zhang
Summary: This article develops a news recommendation model based on a sub-attention news encoder to extract finer-grained segment features from news and represent users accurately and exhaustively. The model utilizes CNN and sub-attention mechanism to extract a rich feature matrix from the news text. It also incorporates a multi-head self-attention mechanism and time series prediction for the user's interests. Experimental results demonstrate that the proposed model performs well on various indicators and outperforms other models in terms of convergence speed, providing guidance for future news recommendation system designs.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Quanyu Dai, Xiao-Ming Wu, Lu Fan, Qimai Li, Han Liu, Xiaotong Zhang, Dan Wang, Guli Lin, Keping Yang
Summary: Knowledge graphs are used to address data sparsity and cold start problems in collaborative filtering. This study proposes a novel framework utilizing collaborative and attentive graph convolutional networks to achieve personalized knowledge-aware recommendation.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Huanwen Wang, Yawen Zeng, Jianguo Chen, Ning Han, Hao Chen
Summary: In many online recommendation services, predicting user's next behavior based on anonymous sessions is still a challenging problem. Existing methods either model user behavior sequences using RNN or capture relationships among items using GNN, but they ignore the importance of different time intervals in the behavior sequence. To address this, we propose an Interval-enhanced Graph Transformer (IGT) solution that considers both item relations and corresponding time intervals. Experimental results on real-world datasets demonstrate the superiority of IGT over state-of-the-art solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Surong Yan, Chongyang Li, Haosen Wang, Bin Lin, Yixian Yuan
Summary: This paper introduces a Feature Interactive Graph Neural Network for KG-based Recommendation (FIKGRec) to improve the performance of recommendation. The method models the interactions between nodes in the knowledge graph and designs a preference-aware attention mechanism to capture the user's fine-grained preference. Experiments demonstrate that the proposed method outperforms existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Liang Xi, Qiaodan Hu, Han Liu
Summary: This paper proposes a Graph-Embedding-inspired Article Recommendation Model(GE-ARM) that captures the user-article correlation feature embeddings by constructing a Bipartite Graph and updates the correlation and article feature embeddings using Probabilistic Matrix Factorization. Experimental results demonstrate that GE-ARM outperforms other methods on four datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Fan Liu, Zhiyong Cheng, Lei Zhu, Chenghao Liu, Liqiang Nie
Summary: This paper presents an attribute-aware attentive graph convolution network (A(2)-GCN) to address the problem of attribute missing in recommender systems. By constructing a graph and utilizing graph convolution network to learn node representation, this method is able to incorporate associate attributes in user and item representation learning, and filter the message passed from an item to a target user based on attribute information using an attention mechanism.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Education & Educational Research
Xiao-Fan Lin, Zhong-Mei Liang, Kan Kan Chan, Wenyi Li, Xiaolan Ling
Summary: This study developed a mobile augmented reality-integration contextual interactive healthcare training system and found that it significantly improved the learning effects, perceived support, self-efficacies, and reduced anxiety of caregivers during the COVID-19 pandemic compared to traditional e-pamphlet instruction. The findings suggest the importance of utilizing technology-enhanced healthcare education and support for caregivers in the context of infectious disease prevention.
JOURNAL OF COMPUTER ASSISTED LEARNING
(2022)
Article
Education & Educational Research
Xiao-Fan Lin, Gwo-Jen Hwang, Jing Wang, Yue Zhou, Wenyi Li, Jiachun Liu, Zhong-Mei Liang
Summary: This study designed a contextualised reflective mechanism-based AR learning model to assist students in completing scientific inquiry tasks. The experimental results revealed that this approach improved students' inquiry learning performances and higher order thinking tendency, as well as their observation, comparison, exploration, and reflection behavioral patterns during field trips.
INTERACTIVE LEARNING ENVIRONMENTS
(2022)
Review
Education & Educational Research
Guangyu Shi, Kan Kan Chan, Xiao-Fan Lin
Summary: The use of the Internet and technology has had a significant impact on citizen's civic participation. Digital citizenship has become an important area of research in order to improve citizen's engagement in the information society. This systematic review of empirical research conducted in the past decade examined various aspects of digital citizenship, including practices, education, and influencing factors. The research found that demographic factors, internet use factors, psychological factors, and social factors all play a role in predicting an individual's digital citizenship. The findings can provide useful insights for policymakers and educators in developing policies and programs related to digital citizenship.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Green & Sustainable Science & Technology
Ching Sing Chai, Thomas K. F. Chiu, Xingwei Wang, Feng Jiang, Xiao-Fan Lin
Summary: It is now essential for learners to acquire basic literacy and competencies in artificial intelligence (AI). Although educators are designing AI curricula, there is a lack of empirical studies on students' perceptions of learning AI. This study developed a research model integrating the theory of planned behavior and the self-determination theory to explain students' behavioral intention to learn AI. The findings suggest that the design of learning resources, autonomy, and AI for social good significantly predict students' intention to learn AI, providing insights for the development of AI education in schools.
Article
Education & Educational Research
Xiao-Fan Lin, Seng Yue Wong, Wei Zhou, Weipeng Shen, Wenyi Li, Chin-Chung Tsai
Summary: Research found that a specific type of augmented reality-assisted science learning design or support may not be suitable or effective for all students due to differences in cognitive load. This study aimed to identify undergraduate students' profiles of cognitive load in AR-assisted science learning and examine how these profiles relate to self-efficacy and behavior patterns. The findings revealed four profiles of cognitive load and showed that these profiles were associated with differences in self-efficacy and behavior patterns. This study provides insights for the effective design of AR-assisted science learning to match appropriate strategies for different student profiles.
INTERNATIONAL JOURNAL OF SCIENCE AND MATHEMATICS EDUCATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Xiao-Fan Lin, Zhaoyang Wang, Wei Zhou, Guoyu Luo, Gwo-Jen Hwang, Yue Zhou, Jing Wang, Qintai Hu, Wenyi Li, Zhong-Mei Liang
Summary: Research has shown that questioning-based dilemmas and role-play simulation can effectively foster students' AI ethics. This study combined these approaches to design a contextualized dilemma discussion method to develop primary school students' AI ethics. By using augmented reality (AR) guidance and feedback, the proposed approach improved students' learning achievements in AI ethical awareness, ethical reasoning, and higher order thinking tendency.
COMPUTERS & EDUCATION
(2023)
Article
Education & Educational Research
Xiao-Fan Lin, Yue Zhou, Weipeng Shen, Guoyu Luo, Xiaoqing Xian, Bo Pang
Summary: K-12 artificial intelligence (AI) education aims to cultivate students' computational thinking in order to apply it to various problems and real-world contexts. This study examined the relationship between Chinese secondary school students' computational thinking efficacy in learning AI, their AI literacy, and approaches to learning AI. The results showed that AI literacy positively influenced students' computational thinking efficacy in learning AI, and this relationship was mediated by sophisticated learning approaches. It is important to focus on students' AI literacy and deep approaches in order to develop their high-level computational thinking efficacy in learning AI. Implications for designing the AI curriculum are discussed.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Education & Educational Research
Xiao-Fan Lin, Dandan Tang, Weipeng Shen, Zhong-Mei Liang, Yaner Tang, Chin-Chung Tsai
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION
(2020)
Article
Education & Educational Research
Xiao-Fan Lin, Dandan Tang, Xuanjun Lin, Zhong-Mei Liang, Chin-Chung Tsai
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION
(2019)
Article
Education & Educational Research
Xiao-Fan Lin, Cailing Deng, Qintai Hu, Chin-Chung Tsai
JOURNAL OF COMPUTER ASSISTED LEARNING
(2019)
Article
Education & Educational Research
Xiao-fan Lin, Jyh-Chong Liang, Chin-Chung Tsai, Qintai Hu
AUSTRALASIAN JOURNAL OF EDUCATIONAL TECHNOLOGY
(2018)
Article
Education & Educational Research
Xiaofan Lin, Xiaoyong Hu, Qintai Hu, Zhichun Liu
BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY
(2016)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
