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
Chemistry, Multidisciplinary
Yi Zuo, Shengzong Liu, Yun Zhou
Summary: The diversified tag-aware recommendation model based on graph collaborative filtering aims to optimize both accuracy and diversity by introducing personalized category-boosted negative sampling and adversarial learning modules. Experiments on the Movielens dataset demonstrate that the model can provide diverse recommendations while maintaining a high level of accuracy.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Cybernetics
Kangqu Zhou, Chen Yang, Lvcheng Li, Cong Miao, Lijun Song, Peng Jiang, Jiafu Su
Summary: This paper proposes a recommendation method that combines semantic relationship and collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities. The experimental results show that the proposed method outperforms other algorithms in terms of mean absolute error (MAE) and F1.
Article
Computer Science, Information Systems
Biwei Yan, Anming Dong, Baobao Chai, Yubing Han, Guanglin Zhou, Fangxin Zhao
Summary: With the rapid development of cloud computing, a large number of web services have emerged quickly, posing a burden for users to choose preferred services. Recommendation algorithms are necessary for suggesting web services, but most existing schemes are based on centralized historical data, risking single point of failure. Secure data sharing among cloud platforms is necessary for better recommendation, which can maximize profits. A blockchain-assisted collaborative service recommendation scheme is proposed, ensuring data confidentiality and integrity, avoiding attacks and failures, and improving recommendation accuracy.
Article
Computer Science, Information Systems
Xuwei Pan, Xuemei Zeng, Ling Ding
Summary: The purpose of this study is to enhance the effectiveness and efficiency of personalized recommendations for social tagging by incorporating topic optimization into collaborative filtering. The recommendation process is divided into offline topic optimization and online recommendation service. Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations. The proposed method achieves improvements in both effectiveness and efficiency for recommendation in social tagging.
DATA TECHNOLOGIES AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Jing Yi, Xubin Ren, Zhenzhong Chen
Summary: In this article, a multi-auxiliary augmented collaborative variational auto-encoder (MA-CVAE) is proposed for tag recommendation, which integrates collaborative and multi-auxiliary information using deep learning. The model predicts tag recommendation probabilities by utilizing latent embeddings and constrains the generation through reconstruction losses, providing recommendations for new items. Additionally, a variational graph auto-encoder is designed to infer latent embeddings of new items using social information.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mingsong Mao, Sihua Chen, Fuguo Zhang, Jialin Han, Quan Xiao
Summary: This paper proposes a hybrid model that combines taxonomy and folksonomy information to enhance ecommerce recommendations. By utilizing tree matching algorithm and random walk model on a heterogeneous graph, the proposed model improves recommendation performance in terms of coverage and accuracy, especially for sparse data.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Xianyu Zhang, Xinguo Ming, Yuguang Bao
Summary: In order to meet customers' personalized and diversified needs, enterprises need to shift from traditional mass manufacturing to mass personalization. Collaborative manufacturing based on stakeholders can address issues such as information asymmetry and low operational efficiency in the manufacturing process. Additionally, the new model of open community manufacturing facilitates the planning and management of networked and social resources. This paper studies the online merchant resource allocation and matching in open community collaborative manufacturing for mass personalization, aiming to improve design efficiency and reduce resource costs.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Computer Science, Information Systems
Erik Quintanilla, Yogesh Rawat, Andrey Sakryukin, Mubarak Shah, Mohan Kankanhalli
Summary: This paper addresses personalized tag recommendation and proposes an end-to-end deep network trained on large-scale datasets. By jointly training user-preference and visual encoding, the network efficiently integrates visual preference with tagging behavior for better user recommendation.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Artificial Intelligence
Gianni Costa, Riccardo Ortale
Summary: There are several challenging issues in the expert recommendation for community question answering that needs to be addressed, such as dynamicity, comprehensive profiling, incorporation of auxiliary data, and manipulation of heterogeneous information. A unified treatment of these issues is believed to improve recommendation effectiveness.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Li Li, Zhongqun Wang, Chen Li, Linjun Chen, Yong Wang
Summary: In this study, a novel collaborative filtering recommendation technique (CFR-F) is proposed to defend against shilling attacks. Experimental results demonstrate that the approach can recommend accurate information resources with lower Mean Absolute Error (MAE) and Average Prediction Shift (APS) compared to traditional techniques.
CONNECTION SCIENCE
(2022)
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
Computer Science, Artificial Intelligence
Beilun Wang, Haoqing Xu, Chunshu Li, Yuchen Li, Meng Wang
Summary: This paper proposes a Knowledge-enhanced Tag-aware Recommendation System (KTRS) that incorporates auxiliary knowledge to improve the performance of tag-aware recommendation systems. Experimental results demonstrate that the proposed system outperforms other recommendation methods on real-world datasets, highlighting the effectiveness of auxiliary knowledge.
KNOWLEDGE-BASED SYSTEMS
(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, Information Systems
Kosar Seyedhoseinzadeh, Hossein A. Rahmani, Mohsen Afsharchi, Mohammad Aliannejadi
Summary: This paper proposes an improved recommender system by incorporating social, geographical, and temporal information into matrix factorization technique. The system achieves better performance on two real-world datasets by modeling social influence and considering users' friendships.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)