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A knowledge-enhanced interest segment division attention network for click-through rate prediction

Neural computing and Applications 2024年第34期36卷 21817-21837页

作者： Liu, Zhanghui Chen, Shijie Chen, Yuzhong Su, Jieyang Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism 5

Image Denoising Method Based on Channel Compression and Spli...

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5th International Conference on Computer Engineering and intelligent Control, ICCEIC 2024

作者： Sun, Hao Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Shandong Yantai China

ISBN: (纸本)9798331507992

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks. © 2024 IEEE.

关键词： Restoration

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EXISTENCE AND UNIQUENESS OF SOLUTIONS FOR UNCERTAIN NONLINEAR SWITCHED SYSTEMS WITH V-N JUMPS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期87卷 129-140页

作者： Jia, Zhifu Li, Cunlin School of Mathematics and Physics Suqian University Suqian223800 China [Ningxia Key Laboratory of intelligent information and Big Data Processing Governance and Social Management Research Center of Northwest Ethnic Regions North Minzu University Yinchuan China

In this paper, an uncertain nonlinear switched system with V-n jumps, characterized by its sensitivity to subjective uncertainties, is modeled using uncertain differential equations with V-n jumps. To account for the discontinuous jump behavior in each subsystem, a V-n jumps process associated with an uncertain Z-n jumps variable, defined by a jump uncertainty distribution, is introduced. Under the assumptions of linear growth and Lipschitz conditions, an existence and uniqueness theorem for the solutions of uncertain nonlinear switched systems with V-n jumps is established and rigorously proven. An illustrative example is provided to demonstrate the effectiveness and applicability of the theoretical results. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Uncertain systems

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An Answer Summarization Scheme Based on Multilayer Attention Model 26

An Answer Summarization Scheme Based on Multilayer Attention...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Xiaolong Dong, Yihao Song, Jian Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China

ISBN: (纸本)9798350331684

At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In CQA, the answer summary could help users get a complete answer quickly. There are still some problems with the current answer summary scheme, such as semantic inconsistency, repetition of words, etc. In order to solve this, we propose a novel scheme Answer Summarization based on Multi-layer Attention Scheme (ASMAM). Based on the traditional Seq2Seq, we introduce self-attention and multi-head attention scheme respectively during sentence and text encoding, which could improve text representation ability of the model. In order to solve "long distance dependence"of RNN and too many parameters of LSTM, we all use GRU as the neuron at the encoder and decoder sides. Experiments over the Yahoo! Answers dataset demonstrate that the coherence and fluency of the generated summary are all superior to the benchmark model in ROUGE evaluation system. © 2023 IEEE.

关键词： Semantics

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LDMOS-Based Doherty Power Amplifier Design in 5G Mobile Micro Base Stations 5

LDMOS-Based Doherty Power Amplifier Design in 5G Mobile Micr...

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5th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Gao, Mingming Wang, Xinyu Wang, Congying Zhang, Zhongxiang Shan, Shiquan Song, Yonghui College of Electronics and Information Engineering Liaoning Technicial University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Huludao China

ISBN: (纸本)9798331533694

A Doherty Power Amplifier (DPA) has been designed and optimized specifically for compact mobile base station deployment, operating within a frequency range of 3.3 GHz to 3.6 GHz. The amplifier utilizes the proprietary DS10-2092 LDMOS transistors developed by the company for both the carrier and peak power amplifiers. Physical test results indicate that the DPA achieves saturated output power meeting the specified requirements, with a maximum saturated drain efficiency of 41%. The drain efficiency remains stable between 26.3% and 30.7% over a 6 dB power back-off range, demonstrating its superior performance in high-efficiency and broadband applications. © 2025 IEEE.

关键词： Doherty amplifiers

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Towards Long-term Fairness in Interactive Recommendation: A Maximum Entropy Reinforcement Learning Approach

Towards Long-term Fairness in Interactive Recommendation: A ...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Shi, Xiaoyu Liu, Quanliang Xie, Hong Shang, Mingsheng Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing China University of Chinese Academy of Sciences Chongqing School Chongqing China

ISBN: (纸本)9798350304855

This paper considers the problem of maintaining the long-term fairness of item exposure in interactive recommendation systems under the dynamic setting that user preference and item popularity evolve over time. The challenge is that the evolving dynamics of user preference and item popularity in the feedback loop amplify the long-term 'unfairness' of item exposure. To address this challenge, we first formulate a constrained Markov Decision Process (MDP) to capture evolving dynamics of user preference. The proposed constrained MDP imposes long-term fairness requirements via maximum entropy techniques. Moreover, to illuminate the 'unfairness' amplifying effect caused by the evolving dynamic of item popularity in the feedback loop, we design a debiased reward function to eliminate popularity bias in the training data. To this end, the proposed framework can maintain acceptable recommendation accuracy while exposing items as randomly as possible, ensuring long-term benefits for users. Experiments on three datasets demonstrate the effectiveness and superiority of our proposed framework in terms of recommendation performance and fairness. © 2023 IEEE.

关键词： Reinforcement learning

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Intention Recognition Method Based on Resting-state and P300 Task-state Dynamic Brain Functional Network Features 15

Intention Recognition Method Based on Resting-state and P300...

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15th International Conference on Measurement Technology and Mechanical Automation, ICMTMA 2023

作者： Yi, X. Wang, Z.M. Heng, X. School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi 'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

In order to reconfigure its structure from the static state in the vision odd ball task, so as to realize the intention recognition based on the characteristics of the brain functional network. The thesis proposes the intention recognition method based on resting state and P300 task state dynamic brain functional network features. First, the brain connectivity in each time window is constructed into a brain functional network using phase lock value (PLV). Then, extract the global features (global efficiency, transitivity) of the brain functional network, and use Louvain algorithm to obtain the brain functional network community. The experimental results show that in the (100-200) ms of P300 task status, the core nodes are mainly concentrated in the forehead region and the central region, while in the (300-500) ms of P300, the core nodes are concentrated in the temporal lobe. The recognition accuracy based on this method reaches 93%. © Published under licence by IOP Publishing Ltd.

关键词： Brain Function Network Intention Recognition P300 PLV Sliding Windows

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LCS-based Visual Recognition for AGV Guide Paths under Complex Illumination Conditions 15

LCS-based Visual Recognition for AGV Guide Paths under Compl...

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15th International Conference on Measurement Technology and Mechanical Automation, ICMTMA 2023

作者： Wang, Z.M. Li, M. Gao, C. Jin, X.M. School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

Complex lighting is one of the most challenging problems in automatic guided vehicle (AGV) vision recognition system. In order to overcome the influence of uneven illumination on the accuracy and robustness of path recognition, this paper proposes the LCS based visual recognition for AGV guide paths under complex illumination conditions method, which converts the collected image into an invariant image through logarithmic chromaticity space (LCS) to eliminate the influence of illumination, and the minimum average entropy angle is used as the projection angle to generate the invariant image to improve the speed of image conversion to the invariant image. Experimental results show that the proposed method can effectively improve the robustness of AGV vision recognition system under complex lighting conditions. © Published under licence by IOP Publishing Ltd.

关键词： Lighting

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Drug-Protein Interaction Prediction by Fusion of Attention and Graph Neural Network 42

Drug-Protein Interaction Prediction by Fusion of Attention a...

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42nd Chinese Control Conference, CCC 2023

作者： Chen, Tianrui Tuo, Shouheng Feng, Zengyu School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9789887581543

The identification of drug-protein interactions (DTIs) is a critical step in drug development and repositioning. However, detecting these interactions using scientific methods presents a formidable challenge. Existing models often struggle to extract relevant information from drug-protein sequences, and they fail to consider pre-existing interactions between pairs. Here, we introduce a deep learning model, DPAG, based on attention mechanisms and graph neural networks, which overcomes these limitations. DPAG leverages information from both the drug and protein sequences, as well as the association between drug-protein pairs, to predict DTIs. We conducted multiple experiments to evaluate the performance of the model in benchmark datasets, and the results showed that DPAG achieved excellent performance in terms of AUC, AUPR, precision, and recall. Our findings suggest that DPAG holds significant potential as a promising tool for predicting DTIs in drug development and repositioning. Code is available at https://***/veresse/DPAG. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Graph neural networks

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Multimodal Image-Text Representation Learning for Sketch-Less Facial Image Retrieval

Multimodal Image-Text Representation Learning for Sketch-Les...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Dawei Dai Yingge Liu Shiyu Fu Guoyin Wang Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Sketch-less facial image retrieval (SLFIR) framework aims to break the barriers that drawing a high-quality facial sketch requires excellent skills and substantial time, it performs the retrieval using a partial sketch with as few strokes as possible. However, such early-stage sketches often contain only local details, resulting in poor retrieval performance. In this study, we propose learning of the representation by fusing the sketches with prior human semantic knowledge to improve the early retrieval performance. Specifically, (1) based on the LAION-Face dataset, a facial language-image pretraining (FLIP) model is constructed to learn the aligned representations of facial image and text; (2) subsequently, using FLIP as the backbone, multiscale features of sketch and text are extracted and fused to learn the efficient representation for the final retrieval. The proposed method achieves state-of-the-art early retrieval performance on all two public datasets and exhibits a good generalization ability in practical testing.

关键词： Representation learning Semantics Image retrieval Feature extraction Faces Testing

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