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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Xinge Wang, Fayu Chen, Hongtao Tianjin University of Technology Tianjin Key Laboratory of Intelligence Intelligence Computing and Novel Software Technology Tianjin China

ISBN: (纸本)9781665410205

Click-through rate prediction plays a crucial role in contemporary recommender systems, which aim to refine recommended items by predicting the click-through rate to enhance the overall recommendation effectiveness. However, existing click-through rate prediction models still do not fully leverage both long-term and short-term user behavior information. Consequently, this paper proposes a novel click-through rate prediction model, named Interest Evolutionary Network with Hash and Multiple Attention Network (IEHAN). The model categorizes user behaviors into long-term and short-term categories, employing a hash sampling method to model long-term user behaviors. By directly filtering behavioral items with the same hash signature as the target item from the entire sequence, the IEHAN model effectively models long-term user interests, thereby reducing information loss. Additionally, the model employs the multi-head attention mechanism and the GRU model with an attention update gate to integrate the relevance of the user's interest state and the target item, thereby enhancing the impact of relevant interests on interest evolution and shaping short-term user interest. Experimental results demonstrate that the IEHAN model achieves higher AUC and lower LOSS evaluation metrics on the Taobao dataset and the Books subset of the Amazon dataset, surpassing other existing models and exhibiting superior prediction accuracy. © 2024 IEEE.

关键词： Prediction models

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Exploring implicit influence for social recommendation based on GNN

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Soft computing 2024年第19期28卷 11001-11014页

作者： Liu, Zhewei Yang, Peilin Hao, Qingbo Zheng, Wenguang Xiao, Yingyuan Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

In recent years, the method of using graph neural networks (GNN) to learn users’ social influence has been widely applied to social recommendation and has shown effectiveness, but several important challenges have not been well addressed: (i) Most work fails to consider the user interests (historical user-item interactions) when first building user-user social relationships, which can make it difficult to capture accurate user embedding and thus prevent the model from better exploring the users’ social influence;(ii) Most of the current methods do not build social neighbors (with the same item-user interaction) that belong to the item and do not aggregate information from the perspective of social neighbors, which makes it possible for the item to lose a lot of details when expressing the user’s interest factors. Therefore, to address the above challenges, we propose Exploring Implicit Influence for Social Recommendation Based on GNN (EIIGNN). First, we construct the initial user embedding with user-item interaction information and use the implicit modeling module in user modeling to explore the implicit influence of interest factors on users. In addition, EIIGNN models the social graph structure of item (an item-item graph) so that item can aggregate information from the perspective of their social neighbors, which helps the model learn a more accurate representation of the item. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of EIIGNN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graph neural networks

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A novel policy iteration algorithm for solving the optimal consensus control problem of a discrete-time multiagent system with unknown dynamics

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Science China(Information Sciences) 2023年第8期66卷 269-270页

作者： Wenkai XU Li WANG Shiwen SUN Chengyi XIA Zengqiang CHEN Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology The Engineering Research Center of Learning-Based Intelligent System Ministry of Education College of Artificial Intelligence Nankai University

At present, an increasing number of researchers have noticed the importance of optimal consensus control(OCC) of multiagent systems(MASs) because of their rich practical applications in various areas [1–4]. To accomp...

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Streaming Session Recommendation Based on User's Global Attributes 28

Streaming Session Recommendation Based on User's Global Attr...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Zhao, Xuechang Yu, Qing Wang, Yifan Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China

ISBN: (纸本)9798350300482

Session-based recommendations are designed to predict the next item a user will click on. It is able to maintain the session structure of the session data. In practical scenarios, session data is dynamic and rapidly generated, which suggests the streaming nature of session data. Recent studies have shown that graph neural network (GNN) based approaches mainly focus on the current session and cannot process the latest session data. In this paper, we propose a streaming session-based recommendation system (UGNN). We use the Wasserstein model for data sampling. Then, the user configuration is embedded as a global attribute in the session graph and incorporated into the embedding vector of the recommendation system. Meanwhile, since the residual network of the Transformer model can effectively alleviate the pressure during model updates, we innovatively added Transformers to the model. Experiments on two real-world datasets show that our model outperforms state-of-the-art models. © 2023 IEEE.

关键词： Recommender systems

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Scoring prediction model focusing on local feature interaction of text 26

Scoring prediction model focusing on local feature interacti...

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

作者： Li, Haoqian Song, Jiayi Zheng, Wenguang Xiao, Yingyuan Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China

ISBN: (纸本)9798350331684

In the era of information explosion, recommender systems are widely studied and applied to explore users' preferences. Reviews often reflect users' real thoughts and play an important role in modeling user preferences. However, most existing review-based recommendation methods assign the same weight to words with different levels of importance, which degrades the recommendation performance. To address these problems, we propose a novel model that uses an adjusted cosine similarity to compute the preference matrix of user, then uses an incentive compression network to weight the preference matrix to focus on important local interaction features and achieve user rating prediction of items based on that interaction feature. The experimental results show that our model outperforms previous mainstream recommendation models. © 2023 IEEE.

关键词： Recommender systems

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Adversarial image detection based on the maximum channel of saliency maps

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Optoelectronics Letters 2022年第5期18卷 307-312页

作者： FU Haoran WANG Chundong LIN Hao HAO Qingbo Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China

Studies have shown that deep neural networks(DNNs) are vulnerable to adversarial examples(AEs) that induce incorrect behaviors. To defend these AEs, various detection techniques have been developed. However, most of them only appear to be effective against specific AEs and cannot generalize well to different AEs. We propose a new detection method against AEs based on the maximum channel of saliency maps(MCSM). The proposed method can alter the structure of adversarial perturbations and preserve the statistical properties of images at the same time. We conduct a complete evaluation on AEs generated by 6 prominent adversarial attacks on the Image Net large scale visual recognition challenge(ILSVRC) 2012 validation sets. The experimental results show that our method performs well on detecting various AEs.

关键词： networks neural maps

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Unsupervised image-to-image translation by semantics consistency and self-attention

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Optoelectronics Letters 2022年第3期18卷 175-180页

作者： ZHANG Zhibin XUE Wanli FU Guokai The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China

Unsupervised image-to-image translation is a challenging task for computer vision. The goal of image translation is to learn a mapping between two domains, without corresponding image pairs. Many previous works only focused on image-level translation but ignored image features processing, which led to a certain semantics loss, such as the changes of the background of the generated image, partial transformation, and so on. In this work, we propose a method of image-to-image translation based on generative adversarial nets(GANs). We use autoencoder structure to extract image features in the generator and add semantic consistency loss on extracted features to maintain the semantic consistency of the generated image. Self-attention mechanism at the end of generator is used to obtain long-distance dependency in image. At the same time, as expanding the convolution receptive field, the quality of the generated image is enhanced. Quantitative experiment shows that our method significantly outperforms previous works. Especially on images with obvious foreground, our model shows an impressive improvement.

关键词： translation features semantic

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LigCDnet:Remote Sensing Image Cloud Detection Based on Lightweight Framework 30th

LigCDnet:Remote Sensing Image Cloud Detection Based on Ligh...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Su, Baotong Zheng, Wenguang School of Computer Science and Engineering Tianjin University of Technology TianJin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology TianJin300384 China

ISBN: (纸本)9783031533075

Cloud contamination is inevitable in remote sensing images, resulting in a large number of images that cannot be applied in various fields. Therefore, cloud detection is one of the important tasks in remote sensing image preprocessing, aimed at removing images obstructed by clouds. Most existing methods are mostly based on CNN and feature a complex network structure, requiring a significant amount of computational resources, making it challenging to deploy them in practical applications. To tackle this problem, we propose a lightweight cloud detection framework (LigCDnet) with a lightweight feature extraction module (LFEM), a channel attention module (CAM), and a lightweight feature pyramid module (LFPM). The LFEM serves as the backbone of the network to capture rich spatial and contextual information;the CAM adaptively adjusts the channel weights of the feature maps;and the LFPM extracts cloud features at multiple scales. The effectiveness of our approach is evaluated on two public datasets, GF-1 and LandSat8. Extensive experiments have demonstrated that the proposed LigCDnet achieves state-of-the-art detection accuracy while significantly reducing computational burden and having a smaller model size. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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TSEGformer: Time-Space Dimension Dependency Transformer for Use in Multivariate Time Series Prediction 24th

TSEGformer: Time-Space Dimension Dependency Transformer for ...

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24th International Conference on Web Information Systems Engineering, WISE 2023

作者： Feng, Yuan Yu, Qing Tianjin University of Technology TUT Tianjin300382 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China

ISBN: (纸本)9789819972531

Multivariate time series (MTS) prediction has always been an important part of sequence prediction. Recently, many researchers have proposed many deep learning models for multivariate time series prediction. Transformer-based models have shown great potential in this regard, as they can better capture the long-term dependencies between sequences, which has great advantages in sequence prediction tasks. However, many existing models focus on encoding and embedding time positions when processing time series, ignoring the dependencies between different dimensions at different times. MTS is not only related in the temporal dimension, but also in the spatial dimension, therefore we propose the TSEGformer. This is a transformer-based model that considers not only temporal and positional information, but also information between different dimensions in the sequence embedding section. Dimension Segment Mean Fusion (DSMF) is proposed, and the input MTS is embedded into a new 2D vector matrix by a module containing temporal and spatial information. Two Part Attention (TPA) layer has also been proposed to effectively capture the relationships between sequences across time and space dimensions. We established the Encoder-Decoder framework and conducted experiments on 5 real datasets, which yielded impressive results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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keywords extraction method based on two-way feature fusion 2

Keywords extraction method based on two-way feature fusion

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2nd International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2022

作者： Li, Yongrui Ning, Hongyun Tianjin University Of Technology Tianjin Key Laboratory Of Intelligence Computing & New Software Technology Tianjin China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

In order to improve the accuracy of keyword extraction, an improved method was proposed to solve the problem of missing keywords in traditional TF-IDF keyword extraction algorithm. In this method, two routes are used to extract the features of the text, one is responsible for storing the dependency syntactic relations in the preprocessing of the text, and the other is responsible for calculating the tF-IDF value, part of speech and position of the words, and constructing the comprehensive scoring weighting formula of the candidate keywords. Finally, the feature information of these two paths is fused in the stage of combinatorial optimization and sorted to form formal extracted keywords. Experimental results show that compared with the traditional TF-IDF algorithm and Textrank algorithm, the F value of the proposed method in Sina news data set is improved by 14.4% and 16.3% respectively, which effectively verifies that the proposed method can significantly improve the keyword extraction performance. © 2022 IEEE.

关键词： Syntactics

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