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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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UNRAVELING THE ENIGMA OF DOUBLE DESCENT: AN IN-DEPTH ANALYSIS THROUGH THE LENS OF LEARNED FEATURE SPACE 12

UNRAVELING THE ENIGMA OF DOUBLE DESCENT: AN IN-DEPTH ANALYSI...

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12th International Conference on Learning Representations, ICLR 2024

作者： Gu, Yufei Zheng, Xiaoqing Aste, Tomaso School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Department of Computer Science University College London United Kingdom

Double descent presents a counter-intuitive aspect within the machine learning domain, and researchers have observed its manifestation in various models and tasks. While some theoretical explanations have been proposed for this phenomenon in specific contexts, an accepted theory for its occurring mechanism in deep learning remains yet to be established. In this study, we revisit the phenomenon of double descent and demonstrate that the presence of noisy data strongly influences its occurrence. By comprehensively analysing the feature space of learned representations, we unveil that double descent arises in imperfect models trained with noisy data. We argue that while small and intermediate models before the interpolation threshold follow the traditional bias-variance trade-off, over-parameterized models interpolate noisy samples among robust data thus acquiring the capability to separate the information from the noise. The source code is available at https://***/Yufei-Gu-451/double_descent_***. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Economic and social effects

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Enhancing NSGA-II with a Knee Point for Constrained Multi-objective Optimization 13th

Enhancing NSGA-II with a Knee Point for Constrained Multi...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Pang, Lie Meng Ishibuchi, Hisao Nan, Yang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635054

To handle constrained multi-objective optimization problems (CMOPs), many constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed. However, a recent study has shown that many of these CMOEAs do not perform well on real-world CMOPs. In contrast, NSGA-II, proposed over 20 years ago with a simple constrained dominance principle, has demonstrated better performance than recent CMOEAs on many real-world CMOPs. Motivated by NSGA-II’s promising results, this paper aims to further enhance its performance. We explore the idea of enhancing NSGA-II with a knee point for solving CMOPs. Specifically, a knee point is identified using the minimum distance from the estimated ideal point, and always included in the next population of NSGA-II. Experimental results show that this simple idea improves the performance of NSGA-II on real-world CMOPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Visual Explanations of Some Problematic Search Behaviors of Frequently-Used EMO Algorithms 1

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Ishibuchi, Hisao Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819635382

ISBN: (纸本)9789819635375

Recently, evolutionary multi-objective optimization (EMO) algorithms have been used in various application fields. Whereas many new EMO algorithms are proposed every year, well-known EMO algorithms such as NSGA-II, MOEA/D, SMS-EMOA and NSGA-III have been still frequently used in application papers as shown by their number of citations. Since most EMO algorithms have been designed for and evaluated by artificial test problems such as DTLZ and WFG, they do not always work well on real-world problems. Main difficulties of real-world problems are as follows: (i) their Pareto front shapes are irregular, (ii) some objectives have much larger values than others, (iii) some decision variables have much larger values than others, (iv) some objectives are highly correlated, and (v) solution evaluation is expensive. In this paper, we focus on the first four difficulties (except for expensive solution evaluation). Our goal is to clearly explain problematic search behaviors of NSGA-II, MOEA/D, SMS-EMOA and NSGA-III caused by these difficulties using simple distance minimization problems. We formulate various distance minimization problems with the above-mentioned four difficulties by changing the location of each target point on the map as well as the size and shape of the map. We also suggest simple remedies for performance improvement of each algorithm for real-world applications without changing the algorithm itself. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Heterogeneous Domain Adaptation via Correlative and Discriminative Feature Learning

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IEEE Transactions on Multimedia 2025年 27卷 3447-3461页

作者： Lu, Yuwu Lin, Dewei Shen, Linlin Zhou, Yicong Pan, Jiahui South China Normal University School of Artificial Intelligence Foshan528225 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518055 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China University of Nottingham Ningbo China Department of Computer Science Ningbo315100 China University of Macau Department of Computer and Information Science China

Heterogeneous domain adaptation seeks to learn an effective classifier or regression model for unlabeled target samples by using the well-labeled source samples but residing in different feature spaces and lying different distributions. Most recent works have concentrated on learning domain-invariant feature representations to minimize the distribution divergence via target pseudo-labels. However, two critical issues need to be further explored: 1) new feature representations should be not only domain-invariant but also category-correlative and discriminative, and 2) alleviating the negative transfer caused by the incorrect pseudo-labeling target samples could boost the adaptation performance during the iterative learning process. To address these issues, in this paper, we put forward a novel heterogeneous domain adaptation method to learn category-correlative and discriminative representations, referred to as correlative and discriminative feature learning (CDFL). Specifically, CDFL aims to learn a feature space where class-specific feature correlations between the source and target domains are maximized, the divergences of marginal and conditional distribution between the source and target domains are minimized, and the distances of inter-class distribution are forced to be maximized to ensure the discriminative ability. Meanwhile, a selective pseudo-labeling procedure based on the correlation coefficient and classifier prediction is introduced to boost class-specific feature correlation and discriminative distribution alignment in an iteration way. Extensive experiments certify that CDFL outperforms the state-of-the-art algorithms on five standard benchmarks. © 1999-2012 IEEE.

关键词： Contrastive Learning

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The optimization of deep learning classification through the comparison between different algorithm by using LABR 28th

The optimization of deep learning classification through the...

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28th International Conference on Knowledge Based and intelligent information and Engineering Systems, KES 2024

作者： Kadhem, Rasha Mallat, Souheyl Zrigui, Mounir Department of Computer Science Faculty of Sciences of Monastir University of Monastir Tunisia Research Laboratory in Algebra Numbers theory and Intelligent Systems Monastir Tunisia Department of Computer Science University of ALQadisiyah Iraq

The deep learning model depends on the specific requirements of our recommender system and the characteristics of the Arabic textual data we are working with it Consider experimenting with multiple models to determine which one performs best for our particular use case, in our case we used Large Arabic Book Review (LABR) is a large sentiment analysis dataset for the Arabic language. It consists of over 63,000 book reviews and Other Datasets such as Amazon Products reviews that we can translate to Arabic and then use for the task, each dataset are tested by three models of deep learning (CNN, RNN,LSTM)and classified by three method (rating, polarity, balance polarity)classification for three type of feature extraction (Bow, TF-IDF, word embedding) after do train 70% and test 30% we get based on the results and the task at hand, if CNN (Convolutional Neural Network) has proven in both training and testing phases to be the most effective among LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network). © 2024 The Authors.

关键词： Convolutional neural networks

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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Internet of Things Challenges and the Emerging Technology of TinyML 19

Internet of Things Challenges and the Emerging Technology of...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Tsoukas, Vasileios Gkogkidis, Anargyros Kakarountas, Athanasios University of Thessaly Intelligent Systems Laboratory Department of Computer Science and Biomedical Informatics Lamia Greece

ISBN: (纸本)9798350346497

The merger of Internet of Things (IoT) and Machine Learning undoubtedly offers several benefits, but due to the nature of IoT devices and their underlying design, a number of difficulties and challenges exist. The most fundamental of these has to do with the security dangers posed by the transmission of voluminous amounts of data, as well as several networking obstacles, such as delay and bandwidth constraints. Moving all processing close to the network's edge and preventing the transmission of data to third parties, such as the cloud or other devices, is a potential solution. Embedded Machine Learning, and TinyML in particular, is a new technology that allows the implementation of Machine Learning on hardware with limited resources, such as Microcontrollers and FPGAs. In this paper, the fundamental issues of IoT devices are examined, the TinyML technology's advantages are outlined, and a summary of the most recent TinyML apps and systems is offered. © 2023 IEEE.

关键词： Microcontrollers

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Simulation of magnetization process and Faraday effect of magnetic bilayer films

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Chinese Physics B 2024年第9期33卷 590-597页

作者： Sheng Gao An Du Lei Zhang Tian-Guang Li Da-Cheng Ma Department of Basic and General Studies Shenyang Institute of Science and TechnologyShenyang 110167China College of Science Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Office of Academic Research Shenyang Institute of Science and TechnologyShenyang 110167China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy ofSciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110016China

We described ferromagnetic film and bilayer films composed of two ferromagnetic layers coupled through antiferromagnetic interfacial interaction by classical Heisenberg model and simulated their magnetization state,magnetic permeability,and Faraday effect at zero and finite temperature by using the Landau–Lifshitz–Gilbert(LLG)*** results indicate that in a microwave field with positive circular polarization,the ferromagnetic film has one resonance peak while the bilayer film has two resonance ***,the resonance peak disappears in ferromagnetic film,and only one resonance peak emerges in bilayer film in the negative circularly polarized microwave *** the microwave field’s frequency exceeds the film’s resonance frequency,the Faraday rotation angle of the ferromagnetic film is the greatest,and it decreases when the thickness of the two halves of the bilayer is *** the microwave field’s frequency remains constant,the Faraday rotation angle fluctuates with temperature in the same manner as spontaneous magnetization *** a DC magnetic field is applied in the direction of the anisotropic axis of the film,the Faraday rotation angle varies with the DC magnetic field and shows a similar shape of the hysteresis loop.

关键词： magnetic bilayer films magnetic permeability hysteresis loop Faraday effect Landau-Lifshitz-Gilbert(LLG)equation

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