Article
Chemistry, Multidisciplinary
Min Ma, Qiong Cao, Xiaoyang Liu
Summary: This study proposed a graph convolution collaborative filtering recommendation method integrating social relations, and experimental results show that this method outperforms existing algorithms in accuracy.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Da Cao, Xiangnan He, Lianhai Miao, Guangyi Xiao, Hao Chen, Jiao Xu
Summary: With the rise of social networks, group activities have become an essential part of daily life. However, issues in group recommendation persist due to factors such as group member weights, social followee information, and user-item interactions. This study proposes neural network-based solutions to address these challenges effectively.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Jiajia Chen, Xin Xin, Xianfeng Liang, Xiangnan He, Jun Liu
Summary: Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. Existing graph-based methods fail to consider the bias offsets of users (items). We propose Graph-Based Decentralized Collaborative Filtering for Social Recommendation (GDSRec) which treats biases as vectors and incorporates them into the learning process of user and item representations. Experimental results show that GDSRec achieves superior performance compared with state-of-the-art related baselines.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yuefang Gao, Zhen-Wei Huang, Zi-Yuan Huang, Ling Huang, Yingjie Kuang, Xiaojun Yang
Summary: Recently, neighborhood-based collaborative filtering has been used more and more in personalized recommender systems. However, the traditional approach of selecting a fixed number of nearest users/items as neighbors has limitations. To address this issue, a new recommender system called Multi-scale Broad Collaborative Filtering (MBCF) is proposed, which captures rich information from different numbers of nearest users/items. Instead of using deep neural networks (DNNs), the Broad Learning System (BLS) is adopted to learn the complex nonlinear relationships between users and items, achieving satisfactory recommendation performance while avoiding overfitting. Extensive experiments on eight benchmark datasets demonstrate the effectiveness of the proposed MBCF algorithm.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Haichi Huang, Xuan Tian, Sisi Luo, Yanli Shi
Summary: This study proposes a novel item-item interaction sequential graph to globally aggregate hidden interaction sequences among all items, and an item-enhanced graph collaborative network (IEGCN) to mix item-item sequences with user-item interactions for collaborative filtering. Experiments show substantial improvements in recall and normalized discounted cumulative gain with IEGCN.
Article
Construction & Building Technology
Yao Zhang, Shuangliang Tai, Kunhui Ye
Summary: This study introduces and verifies a credit network recommendation model based on a collaborative filtering algorithm, which can help owners select efficient contractors with high credit, thereby promoting the healthy development of the construction market.
Article
Computer Science, Artificial Intelligence
Xinyu Xiao, Junhao Wen, Wei Zhou, Fengji Luo, Min Gao, Jun Zeng
Summary: This paper proposes a graph social fusion recommendation method, which can capture multiple social information simultaneously and dynamically adjust user interest weights. It utilizes a dynamic attention mechanism to capture interactions in subgraphs, representing changes in user interests in heterogeneous networks. A mutualistic mechanism is combined to simulate the mutually reinforcing relationship between social behavior and virtual behavior.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Yanheng Liu, Minghao Yin, Xu Zhou
Summary: A POI group recommendation method based on collaborative filtering with intragroup divergence is proposed in this paper. The method improves recommendation precision by constructing user preference vectors, calculating preference degrees, establishing feature preference models, measuring intragroup divergence, and computing preference ratings. Experimental results show the superior performance of the proposed method.
APPLIED SCIENCES-BASEL
(2021)
Article
Automation & Control Systems
Le Wu, Peijie Sun, Richang Hong, Yong Ge, Meng Wang
Summary: The paper proposes a collaborative neural social recommendation (CNSR) model that combines the social embedding part and the collaborative neural recommendation (CNR) part, successfully addressing the challenges in social recommendation and demonstrating high recommendation effectiveness.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hao Tang, Guoshuai Zhao, Xuxiao Bu, Xueming Qian
Summary: The recommendation system is an important technology in the era of Big Data. Current methods have integrated side information to alleviate the sparsity problem, but not all side information can be obtained with high quality. By proposing the DMGCF model and dynamically evolving multi-graph collaborative filtering, the approach successfully mines and reuses side information, as demonstrated by experimental results.
KNOWLEDGE-BASED SYSTEMS
(2021)
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
Hospitality, Leisure, Sport & Tourism
Jia-Li Chang, Hui Li, Jian-Wu Bi
Summary: This study proposes a hybrid method incorporating multi-attribute collaborative filtering and social network analysis to produce personalized travel recommendations, improving travellers' online booking experience. The method includes modules for identifying online opinion experts, constructing a social network, detecting user communities, and interactively generating personalized recommendations. With this method, travellers are presented with a more appropriate set of options, leading to better travel decisions.
CURRENT ISSUES IN TOURISM
(2022)
Article
Computer Science, Artificial Intelligence
Liping Wang, Wei Zhou, Ling Liu, Zhengyi Yang, Junhao Wen
Summary: In this paper, we propose a Deep Adaptive Collaborative Graph Neural Network for Social Recommendation (DUI-SoRec), which addresses the issues of social inconsistency and over-smoothing in GCN-based recommender systems. By generating two subgraphs and utilizing a deep adaptive graph neural network, the model learns user and item embeddings effectively. The model's effectiveness is demonstrated through extensive experiments on real-world datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Zhuo Cai, Guan Yuan, Shaojie Qiao, Song Qu, Yanmei Zhang, Rui Bing
Summary: This paper proposes a novel framework called Friends-aware Graph Collaborative Filtering (FG-CF), which incorporates social information into the user-POI graph. By considering social ties and contextual information, the framework improves the accuracy and effectiveness of personalized recommendation.
Article
Computer Science, Information Systems
Wei Wang, Tao Tang, Feng Xia, Zhiguo Gong, Zhikui Chen, Huan Liu
Summary: This article proposes a collaborative filtering with network representation learning framework, called CNCRec, for citation recommendation. By utilizing attributed citation network representation learning and the learned representations of attributed collaboration network, CNCRec can accurately recommend citations in academic information networks and better solve the data sparsity problem.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.