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Learning-based counterfactual explanations for recommendation

Science China(Information Sciences) 2024年第8期67卷 115-129页

作者： Jingxuan WEN Huafeng LIU Liping JING Jian YU School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University

Counterfactual explanations provide explanations by exploring the changes in effect caused by changes in cause. They have attracted significant attention in recommender system research to explore the impact of changes in certain properties on the recommendation mechanism. Among several counterfactual recommendation methods, item-based counterfactual explanation methods have attracted considerable attention because of their flexibility. The core idea of item-based counterfactual explanation methods is to find a minimal subset of interacted items(i.e., short length) such that the recommended item would topple out of the top-K recommendation list once these items have been removed from user interactions(i.e., good quality). Usually, explanations are generated by ranking the precomputed importance of items, which fails to characterize the true importance of interacted items due to separation from the explanation generation. Additionally, the final explanations are generated according to a certain search strategy given the precomputed importance. This indicates that the quality and length of counterfactual explanations are deterministic;therefore, they cannot be balanced once the search strategy is fixed. To overcome these obstacles, this study proposes learning-based counterfactual explanations for recommendation(LCER) to provide counterfactual explanations based on personalized recommendations by jointly modeling the factual and counterfactual preference. To achieve consistency between the computation of importance and generation of counterfactual explanations, the proposed LCER endows an optimizable importance for each interacted item, which is supervised by the goal of counterfactual explanations to guarantee its credibility. Because of the model's flexibility, the trade-off between quality and length can be customized by setting different proportions. The experimental results on four real-world datasets demonstrate the effectiveness of the proposed LCER over several

关键词： recommender system explainable recommendation item-based explanation counterfactual inference counterfactual explanation

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DenoiseRep: Denoising Model for Representation Learning 38

DenoiseRep: Denoising Model for Representation Learning

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xu, Zhengrui Wang, Guan'an Huang, Xiaowen Sang, Jitao School of Computer Science and Technology Beijing Jiaotong University China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University China Ministry of Education China

The denoising model has been proven a powerful generative model but has little exploration of discriminative tasks. Representation learning is important in discriminative tasks, which is defined as "learning representations (or features) of the data that make it easier to extract useful information when building classifiers or other predictors" [4]. In this paper, we propose a novel Denoising Model for Representation Learning (DenoiseRep) to improve feature discrimination with joint feature extraction and denoising. DenoiseRep views each embedding layer in a backbone as a denoising layer, processing the cascaded embedding layers as if we are recursively denoise features step-by-step. This unifies the frameworks of feature extraction and denoising, where the former progressively embeds features from low-level to high-level, and the latter recursively denoises features step-by-step. After that, DenoiseRep fus es the parameters of feature extraction and denoising layers, and theoretically demonstrates its equivalence before and after the fusion, thus making feature denoising computation-free. DenoiseRep is a label-free algorithm that incrementally improves features but also complementary to the label if available. Experimental results on various discriminative vision tasks, including re-identification (Market-1501, DukeMTMC-reID, MSMT17, CUHK-03, vehicleID), image classification (ImageNet, UB200, Oxford-Pet, Flowers), object detection (COCO), image segmentation (ADE20K) show stability and impressive improvements. We also validate its effectiveness on the CNN (ResNet) and Transformer (ViT, Swin, Vmamda) architectures. Code is available at https://***/wangguanan/DenoiseRep. © 2024 Neural information processing systems foundation. All rights reserved.

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HEM: An Improved Parametric Link Prediction Algorithm Based on Hybrid Network Evolution Mechanism 9th

HEM: An Improved Parametric Link Prediction Algorithm Based ...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Ke, Dejing Pu, Jiansu University of Electronic Science and Technology of China Sichuan611731 China Big Data Visual Analysis Lab University of Electronic Science and Technology of China Sichuan610000 China

ISBN: (纸本)9783031466632

Link prediction plays an important role in the research of complex networks. Its task is to predict missing links or possible new links in the future via existing information in the network. In recent years, many powerful link prediction algorithms have emerged, which have good results in prediction accuracy and interpretability. However, the existing research still cannot clearly point out the relationship between the characteristics of the network and the mechanism of link generation, and the predictability of complex networks with different features remains to be further analyzed. In view of this, this article proposes the corresponding link prediction indices Reg, DFPA and LW on regular network, scale-free network and small-world network respectively, and studies their prediction properties on these three network models. At the same time, we propose a parametric hybrid index HEM and compare the prediction accuracy of HEM and many similarity-based indices on real-world networks. The experimental results show that HEM performs better than other indices. In addition, we study the factors that play a major role in the prediction of HEM and analyze their relationship with the characteristics of real-world networks. The results show that the predictive properties of factors are closely related to the features of networks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence

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Plant Phenomics 2024年第2期6卷 349-363页

作者： Yandong Wu Weiliang Wen Shenghao Gu Guanmin Huang Chuanyu Wang Xianju Lu Pengliang Xiao Xinyu Guo Linsheng Huang National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Beijing Key Lab of Digital Plant National Engineering Research Center for Information Technology in AgricultureBeijing 100097China Nongxin Science&Technology(Beijing)Co. LtdBeijing 100097China

The 3-dimensional(3D)modeling of crop canopies is fundamental for studying functional-structural plant *** studies often fail to capture the structural characteristics of crop canopies,such as organ overlapping and resource *** address this issue,we propose a 3D maize modeling method based on computational *** initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture.

关键词： intelligence modeling based canopies computational dimensional maize three

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Detecting Community Through User Similarity analysis on Twitter 18

Detecting Community Through User Similarity Analysis on Twit...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Hasan, Md Ahsan Ul Bakar, Azuraliza Abu Yaakub, Mohd Ridzwan Universiti Kebangsaan Malaysia Sentiment Analysis Lab Center for Artificial Intelligence Technology Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Universiti Kebangsaan Malaysia Data Mining and Optimization Lab Center for Artificial Intelligence Technology Faculty of Information Science and Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350331011

Community detection in social relationship networks is a significant research domain within the field of social network analysis. However, Twitter sets itself apart from conventional friends' and followers' networks due to its users' distinct focus on sharing thoughts and ideas related to specific topics rather than prioritising personal connections. As a result, relying solely on friends' and followers' relations in Twitter networks for community detection would be insufficient and incomplete. In this paper, we proposed a Twitter community detection method that measures similarity using user profiles, tweet subjects, and tweet sentiment in this research. We constructed a similarity network using real-time Twitter API data and compared it to Zachary's Karate Network. The proposed approach enhances the quality function of modularity and conductance in the Louvain community detection algorithm, resulting in a more precise and meaningful community structure within the Twitter network. © 2024 IEEE.

关键词： Graph databases

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Enhancing Underwater Images via Asymmetric Multi-Scale Invertible Networks 24

Enhancing Underwater Images via Asymmetric Multi-Scale Inver...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Quan, Yuhui Tan, Xiaoheng Huang, Yan Xu, Yong Ji, Hui South China University of Technology Guangzhou China National University of Singapore Singapore Singapore Pazhou Lab Guangzhou China Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis Guangzhou China

ISBN: (纸本)9798400706868

Underwater images, often plagued by complex degradation, pose significant challenges for image enhancement. To address these challenges, the paper redefines underwater image enhancement as an image decomposition problem and proposes a deep invertible neural network (INN) that accurately predicts both the latent image and the degradation effects. Instead of using an explicit formation model to describe the degradation process, the INN adheres to the constraints of the image decomposition model, providing necessary regularization for model training, particularly in the absence of supervision on degradation effects. Taking into account the diverse scales of degradation factors, the INN is structured on a multi-scale basis to effectively manage the varied scales of degradation factors. Moreover, the INN incorporates several asymmetric design elements that are specifically optimized for the decomposition model and the unique physics of underwater imaging. Comprehensive experiments show that our approach provides significant performance improvement over existing methods. © 2024 ACM.

关键词： Underwater imaging

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Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer 23

Tracing Knowledge Instead of Patterns: Stable Knowledge Trac...

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2023 World Wide Web Conference, WWW 2023

作者： Yin, Yu Dai, Le Huang, Zhenya Shen, Shuanghong Wang, Fei Liu, Qi Chen, Enhong Li, Xin Anhui Key Lab. of Big Data Analysis and Application University of Science and Technology of China State Key Lab. of Cognitive Intelligence Anhui Hefei China University of Science and Technology of China IFLYTEK Co. Ltd China

ISBN: (纸本)9781450394161

Knowledge Tracing (KT) aims at tracing the evolution of the knowledge states along the learning process of a learner. It has become a crucial task for online learning systems to model the learning process of their users, and further provide their users a personalized learning guidance. However, recent developments in KT based on deep neural networks mostly focus on increasing the accuracy of predicting the next performance of students. We argue that current KT modeling, as well as training paradigm, can lead to models tracing patterns of learner's learning activities, instead of their evolving knowledge states. In this paper, we propose a new architecture, Diagnostic Transformer (DTransformer), along with a new training paradigm, to tackle this challenge. With DTransformer, we build the architecture from question-level to knowledge-level, explicitly diagnosing learner's knowledge proficiency from each question mastery states. We also propose a novel training algorithm based on contrastive learning that focuses on maintaining the stability of the knowledge state diagnosis. Through extensive experiments, we will show that with its understanding of knowledge state evolution, DTransformer achieves a better performance prediction accuracy and more stable knowledge state tracing results. We will also show that DTransformer is less sensitive to specific patterns with case study. We open-sourced our code and data at https://***/yxonic/DTransformer. © 2023 ACM.

关键词： Deep neural networks

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Fuzzy-NMS: Improving 3D Object Detection With Fuzzy Classification in NMS

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Li Zhang, Xinyu Zhao, Fachuan Wu, Chuze Wang, Yichen Song, Ziying Yang, Lei Xu, Bin Li, Jun Ge, Shuzhi Sam School of Machanical Engineering Beijing Institute of Technology Beijing China School of Vehicle and Mobility Tsinghua University Beijing China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering Interactive Digital Media Institute Social Robotics Laboratory National University of Singapore Singapore

Non-maximum suppression (NMS) is an essential post-processing module in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments use the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time. IEEE

关键词： Object detection

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WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models

WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark f...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Jian Ning, Kangyun Su, Yisong Han, Wenjuan Xu, Jinan Zhang, Yuanzhe University of Science and Technology Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China College of Computer and Data Science Fuzhou University Fuzhou China National Science Library Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Although Large Language Models (LLMs) excel in NLP tasks, they still need external tools to extend their ability. Current research on tool learning with LLMs often assumes mandatory tool use, which does not always align with real-world situations, where the necessity for tools is uncertain, and incorrect or unnecessary use of tools can damage the general abilities of LLMs. Therefore, we propose to explore whether LLMs can discern their ability boundaries and use tools flexibly. We then introduce the Whether-or-not tool usage Evaluation benchmark (WTU-Eval) to assess LLMs with eleven datasets, where six of them are tool-usage datasets, and five are general datasets. LLMs are prompted to use tools according to their needs. The results of eight LLMs on WTU-Eval reveal that LLMs frequently struggle to determine tool use in general datasets, and LLMs' performance in tool-usage datasets improves when their ability is similar to ChatGPT. © 2025 IEEE.

关键词： Ability Boundary Evaluation Large Language Models Tool Usage

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A Dynamic TDMA Scheduling Based Clustering Algorithm for MANETs 5

A Dynamic TDMA Scheduling Based Clustering Algorithm for MAN...

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2023 5th International Conference on Artificial Intelligence and Computer Applications, ICAICA 2023

作者： Hu, Jiahui Liu, Qiang Li, Li Wu, Qianqian Wu, Zefan School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Transportation Data Analysis and Mining Beijing China Wuhan Ship Communication Research Institute Wuhan China

ISBN: (纸本)9798350323313

With the popularity of wireless devices such as laptops, wireless LAN devices, and wireless sensors, Mobile Ad hoc Networks (MANETs) have attracted increasing attention and extensive research. The challenges of MANET, such as multi-hop routing, dynamic network topology, and scalability, have raised higher requirements for access control protocols. In order to further improve network scalability, we propose a new TDMA protocol (C-TDMA). This protocol introduces the concept of cluster networking based on the USAP protocol. Specifically, by grouping nodes with the same working frequency into virtual clusters and selecting cluster heads based on dynamic and fair weighting methods, the time utilization of data transmission is optimized. In addition, we designed new data structures and frame structures, and used a combination of TDMA and FDMA to divide the channels, improving channel utilization. Simulation studies show that our C- TDMA protocol has better network performance than the USAP protocol as the network scale expands. © 2023 IEEE.

关键词： Mobile ad hoc networks

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