Proceedings of the 31st ACM International Conference on Information & Knowledge Management最新文献_第5页

Spatio-temporal Trajectory Learning using Simulation Systems 利用仿真系统进行时空轨迹学习

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557457

Daniel Glake, Fabian Panse, Ulfia A. Lenfers, T. Clemen, N. Ritter

{"title":"Spatio-temporal Trajectory Learning using Simulation Systems","authors":"Daniel Glake, Fabian Panse, Ulfia A. Lenfers, T. Clemen, N. Ritter","doi":"10.1145/3511808.3557457","DOIUrl":"https://doi.org/10.1145/3511808.3557457","url":null,"abstract":"Spatio-temporal trajectories are essential factors for systems used in public transport, social ecology, and many other disciplines where movement is a relevant dynamic process. Each trajectory describes multiple state changes over time, induced by individual decision-making, based on psychological and social factors with physical constraints. Since a crucial factor of such systems is to reason about the potential trajectories in a closed environment, the primary problem is the realistic replication of individual decision making. Mental factors are often uncertain, not available or cannot be observed in reality. Thus, models for data generation must be derived from abstract studies using probabilities. To solve these problems, we present Multi-Agent-Trajectory-Learning (MATL), a state transition model to learn and generate human-like Spatio-temporal trajectory data. MATL combines Generative Adversarial Imitation Learning (GAIL) with a simulation system that uses constraints given by an agent-based model (Aℬℳ). We use GAIL to learn policies in conjunction with the Aℬℳ, resulting in a novel concept of individual decision making. Experiments with standard trajectory predictions show that our approach produces similar results to real-world observations.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133923163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

eDental: Managing Your Dental Care in Diet Diaries 在饮食日记中管理你的牙齿护理

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557215

Kaiping Zheng, Thao Nguyen, Changshuo Liu, C. E. Goh, B. Ooi

引用次数: 2

Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce 基于图的电子商务语义关联学习弱监督框架

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557143

Zhiyuan Zeng, Yuzhi Huang, Tianshu Wu, Hongbo Deng, Jian Xu, Bo Zheng

{"title":"Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce","authors":"Zhiyuan Zeng, Yuzhi Huang, Tianshu Wu, Hongbo Deng, Jian Xu, Bo Zheng","doi":"10.1145/3511808.3557143","DOIUrl":"https://doi.org/10.1145/3511808.3557143","url":null,"abstract":"Product searching is fundamental in online e-commerce systems, it needs to quickly and accurately find the products that users required. Relevance is essential for e-commerce search, which role is avoiding displaying products that do not match search intent and optimizing user experience. Measuring semantic relevance is necessary because distributional biases between search queries and product titles may lead to large lexical differences between relevant textual expressions. Several problems limit the performance of semantic relevance learning, including extremely long-tail product distribution and low-quality labeled data. Recent works attempt to conduct relevance learning through user behaviors. However, noisy user behavior can easily cause inadequately semantic modeling. Therefore, it is valuable but challenging to utilize user behavior in relevance learning. In this paper, we first propose a weakly supervised contrastive learning framework that focuses on how to provide effective semantic supervision and generate reasonable representation. We utilize topology structure information contained in a user behavior heterogeneous graph to design a semantically aware data construction strategy. Besides, we propose a contrastive learning framework suitable for e-commerce scenarios with targeted improvements in data augmentation and training objectives. For relevance calculation, we propose a novel hybrid method that combines fine-tuning and transfer learning. It eliminates the negative impacts caused by distributional bias and guarantees semantic matching capabilities. Extensive experiments and analyses show the promising performance of proposed methods in relevance learning.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132777121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

CPEE: Civil Case Judgment Prediction centering on the Trial Mode of Essential Elements CPEE:基于基本要件审判模式的民事案件判决预测

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557273

Lili Zhao, Linan Yue, Yanqing An, Yuren Zhang, Jun Yu, Qi Liu, Enhong Chen

{"title":"CPEE: Civil Case Judgment Prediction centering on the Trial Mode of Essential Elements","authors":"Lili Zhao, Linan Yue, Yanqing An, Yuren Zhang, Jun Yu, Qi Liu, Enhong Chen","doi":"10.1145/3511808.3557273","DOIUrl":"https://doi.org/10.1145/3511808.3557273","url":null,"abstract":"Civil Case Judgment Prediction (CCJP) is a fundamental task in the legal intelligence of the civil law system, which aims to automatically predict the judgment results on each plea of the plaintiff. Existing studies mainly focus on making judgment predictions only on a certain civil cause (e.g., the divorce dispute) by utilizing the fact descriptions and pleas of the plaintiff, which still suffer from the various causes and complicated legal essential elements in the real court. Thus, in this paper, we formalize CCJP as a multi-task learning problem and propose a CCJP method centering on the trial mode of essential elements, CPEE, which explores the practical judicial process and analyzes comprehensive legal essential elements to make judgment predictions. Specifically, we first construct three tasks (i.e., the predictions on the civil causes, law articles, and the final judgment on each plea) necessary for CCJP, that follow the judgment process and exploit the results of intermediate subtasks to make judgment predictions. Then we design a logic-enhanced network to predict the results of three tasks and conduct a comprehensive study of civil cases. Finally, owing to the interlinked and dependent relationships among each task, we adopt the cause prediction result to help predict law articles and incorporate them into final judgment prediction through a gate mechanism. Furthermore, since the existing dataset fails to provide sufficient case information, we construct a real-world CCJP dataset that contains various causes and comprehensive legal elements. Extensive experimental results on the dataset validate the effectiveness of our method.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130947070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

SmartQuery: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction SmartQuery:基于混合不确定性减少的图神经网络主动学习框架

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557701

Xiaoting Li, Yuhang Wu, Vineeth Rakesh, Yusan Lin, Hao Yang, Fei Wang

引用次数: 1

Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-Video Recommendation 基于多聚合器时间翘曲异构图神经网络的个性化微视频推荐

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557403

Jinkun Han, Wei Li, Zhipeng Cai, Yingshu Li

引用次数: 1

Subspace Co-clustering with Two-Way Graph Convolution 基于双向图卷积的子空间共聚类

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557706

Chakib Fettal, Lazhar Labiod, M. Nadif

{"title":"Subspace Co-clustering with Two-Way Graph Convolution","authors":"Chakib Fettal, Lazhar Labiod, M. Nadif","doi":"10.1145/3511808.3557706","DOIUrl":"https://doi.org/10.1145/3511808.3557706","url":null,"abstract":"Subspace clustering aims to cluster high dimensional data lying in a union of low-dimensional subspaces. It has shown good results on the task of image clustering but text clustering, using document-term matrices, proved more impervious to advances based on this approach. We hypothesize that this is because, compared to image data, text data is generally higher dimensional and sparser. This renders subspace clustering impractical in such a context. Here, we leverage subspace clustering for text by addressing these issues. We first extend the concept of subspace clustering to co-clustering, which has been extensively used on document-term matrices due to the resulting interplay between the document and term representations. We then address the sparsity problem through a two-way graph convolution, which promotes the grouping effect that has been credited for the effectiveness of some subspace clustering models. The proposed formulation results in an algorithm that is efficient both in terms of computational and spatial complexity. We show the competitiveness of our model w.r.t the state-of-the-art on document-term attributed graph datasets in terms of performance and efficiency.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133283973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

℘-MinHash Algorithm for Continuous Probability Measures: Theory and Application to Machine Learning 连续概率度量的p -MinHash算法:理论及其在机器学习中的应用

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557413

Ping Li, Xiaoyun Li, G. Samorodnitsky

引用次数: 0

Dr. Can See: Towards a Multi-modal Disease Diagnosis Virtual Assistant Dr. Can See:迈向多模式疾病诊断虚拟助手

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557296

Abhisek Tiwari, Manisimha Manthena, S. Saha, P. Bhattacharyya, Minakshi Dhar, Sarbajeet Tiwari

{"title":"Dr. Can See: Towards a Multi-modal Disease Diagnosis Virtual Assistant","authors":"Abhisek Tiwari, Manisimha Manthena, S. Saha, P. Bhattacharyya, Minakshi Dhar, Sarbajeet Tiwari","doi":"10.1145/3511808.3557296","DOIUrl":"https://doi.org/10.1145/3511808.3557296","url":null,"abstract":"Artificial Intelligence-based clinical decision support is gaining ever-growing popularity and demand in both the research and industry communities. One such manifestation is automatic disease diagnosis, which aims to assist clinicians in conducting symptom investigations and disease diagnoses. When we consult with doctors, we often report and describe our health conditions with visual aids. Moreover, many people are unacquainted with several symptoms and medical terms, such as mouth ulcer and skin growth. Therefore, visual form of symptom reporting is a necessity. Motivated by the efficacy of visual form of symptom reporting, we propose and build a novel end-to-end Multi-modal Disease Diagnosis Virtual Assistant (MDD-VA) using reinforcement learning technique. In conversation, users' responses are heavily influenced by the ongoing dialogue context, and multi-modal responses appear to be of no difference. We also propose and incorporate a Context-aware Symptom Image Identification module that leverages discourse context in addition to the symptom image for identifying symptoms effectively. Furthermore, we first curate a multi-modal conversational medical dialogue corpus in English that is annotated with intent, symptoms, and visual information. The proposed MDD-VA outperforms multiple uni-modal baselines in both automatic and human evaluation, which firmly establishes the critical role of symptom information provided by visuals . The dataset and code are available at https://github.com/NLP-RL/DrCanSee","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115756053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

LCD: Adaptive Label Correction for Denoising Music Recommendation LCD:自适应标签校正去噪音乐推荐

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557625

Quanyu Dai, Yalei Lv, Jieming Zhu, Junjie Ye, Zhenhua Dong, Rui Zhang, Shutao Xia, Ruiming Tang

引用次数: 2