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arXiv 2021年

作者： Wu, Le He, Xiangnan Wang, Xiang Zhang, Kun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei230029 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China University of Science and Technology of China Hefei230026 China National University of Singapore Singapore

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers and practitioners working on recommender systems. Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering, which leverages the key source of user-item interaction data;2) content enriched recommendation, which additionally utilizes the side information associated with users and items, like user profile and item knowledge graph;and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative work for each type, we finally discuss some promising directions in this field. © 2021, CC BY.

关键词： Surveys

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Iterative context-aware graph inference for visual dialog

arXiv

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arXiv 2020年

作者： Guo, Dan Wang, Hui Zhang, Hanwang Zha, Zheng-Jun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Nanyang Technological University University of Science and Technology of China

Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts. This task can refer to the relation inference in a graphical model with sparse contexts and unknown graph structure (relation descriptor), and how to model the underlying context-aware relation inference is critical. To this end, we propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations. The graph structure (relations in dialog) is iteratively updated using an adaptive top-K message passing mechanism. Specifically, in every message passing step, each node selects the most K relevant nodes, and only receives messages from them. Then, after the update, we impose graph attention on all the nodes to get the final graph embedding and infer the answer. In CAG, each node has dynamic relations in the graph (different related K neighbor nodes), and only the most relevant nodes are attributive to the context-aware relational graph inference. Experimental results on VisDial v0.9 and v1.0 datasets show that CAG outperforms comparative methods. Visualization results further validate the interpretability of our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Message passing

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Towards efficient local causal structure learning

arXiv

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arXiv 2021年

作者： Yang, Shuai Wang, Hao Yu, Kui Cao, Fuyuan Wu, Xindong The School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China The School of Computer and Information Technology Shanxi University Taiyuan030006 China The Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology Hefei230601 China Mininglamp Academy of Sciences Mininglamp Technology Beijing100102 China

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Bayesian networks

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Iterative Context-Aware Graph Inference for Visual Dialog

Iterative Context-Aware Graph Inference for Visual Dialog

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Dan Guo Hui Wang Hanwang Zhang Zheng-Jun Zha Meng Wang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Nanyang Technological University University of Science and Technology of China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

关键词： Visualization History Task analysis Semantics Message passing Neural networks Cognition

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Combining Global and Sequential Patterns for Multivariate Time Series Forecasting

Combining Global and Sequential Patterns for Multivariate Ti...

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IEEE International Conference on Big data

作者： Zhaoxi Li Jun He Hongyan Liu Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China School of Economics and Management Tsinghua g University Beijing China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Multivariate time series forecasting is very important for many applications. Many studies have been conducted for accurate and interpretable prediction methods. However, existing methods either cannot take both times series and covariates into consideration, lacking of interpretability, or ignore global trends across multivariate time series. In this paper, we aim to solve these issues. To this end, we propose a new model named TEDGE for accurate and interpretable time series prediction. In this model, we extract global trends hidden across multivariate times series to improve prediction accuracy. Meanwhile, we utilize a deep recurrent model with attention mechanism to find long-and short-term sequential patterns hidden in individual time series with interpretability. We conduct experiments on several datasets to evaluate the proposed models performance. Results demonstrate the superior performance of our proposed model.

关键词： Conferences Time series analysis Predictive models Big data Market research data models Forecasting

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Active Learning with Multi-granular Graph Auto-Encoder

Active Learning with Multi-granular Graph Auto-Encoder

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IEEE International Conference on data Mining (ICDM)

作者： Yi He Xu Yuan Nian-Feng Tzeng Xindong Wu Center for Advanced Computer Studies University of Louisiana Lafayette Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Mininglamp Academy of Sciences Mininglamp Technology

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Predictive modeling of networked data finds many real-world applications, such as fraud detection in social networks, drug discovery in biomedical networks, paper topic classification in citation networks, and so forth. Although the advanced machine learning approaches can help build reasonably accurate predictive models, their applicability is immensely hindered by the data labeling tasks, which are onerous, time-consuming, and error-prone. In this paper, we propose a novel active learning paradigm for networked data, named topology-and-content-aware (TACA) active learning, aiming to minimize the number of labels while achieving a desirable level of model accuracy. Overall, TACA advances existing works from two aspects: (1) TACA makes no assumption on the network property, whereas most existing works only perform effectively on a locally consistent network in which linked nodes are expected to share the same labels and (2) TACA generates queries without relying on model performance, thereby enjoying robust predictive results even when noises exist in the queried labels. Both theoretical and empirical evidences are presented, substantiating the effectiveness of and optimism our approach.

关键词： Social networking (online) Neural networks Predictive models data models Labeling Noise measurement Task analysis

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Balanced Tree Partitioning with Succinct Logic

Balanced Tree Partitioning with Succinct Logic

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IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Shaojing Sheng Peng Zhou Key Laboratory of Knowledge Engineering with Big Data (Heifei Unversity of Technology) Ministry of Education Heifei China School of Compuster Science and Technology Anhui University Heifei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

As a widely used data structure, graphs are good at characterizing data with internal associations, such as social and biological data. Tree structured data are special and are widely used in many real-world applications, such as organizational structure analysis and genealogical knowledge graph reasoning. For example, in kinship knowledge graph analysis, when a genealogical tree is particularly large (more than 25 levels and 45,000 nodes), it is a great challenge to partition this large tree into a specified number of subtrees with succinct logic and a balanced number of nodes. Therefore, in this paper, we propose the TPA (tree partitioning algorithm) algorithm to achieve a balanced and succinct logic partition of large-scale tree structured data. TPA first extracts all related nodes from a massive graph database and then constructs the convergent subgraph into a complete tree with a specified root node. Specifically, several virtual nodes are supplemented for generation-skipping connected nodes to achieve correct node numbering and partitioning. Finally, a graph partitioning algorithm is executed on the complete tree to obtain a specified number of subtrees with succinct logic and balanced node scales. Experiments conducted on four real-world datasets verify the effectiveness of our TPA algorithm.

关键词： Partitioning algorithms data visualization Convergence data analysis Distributed databases Big data

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Higgs Boson Discovery using Machine Learning Methods with Pyspark

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Procedia Computer Science 2020年 170卷 1141-1146页

作者： Mourad Azhari Abdallah Abarda Badia Ettaki Jamal Zerouaoui Mohamed Dakkon Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and knowledge Engineering School of Information Sciences Rabat Morocco Département de Statistique et Informatique de Gestion Université Abdelmalek Essaadi Tetouan Morocco

Higgs Boson is an elementary particle that gives the mass to everything in the natural world. The discovery of the Higgs Boson is a major challenge for particle physics. This paper proposes to solve the Higgs Boson Classification Problem with four Machine Learning (ML) Methods, using the Pyspark environment: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and Gradient Boosted Tree (GBT). We compare the accuracy and AUC metrics of those ML Methods. We use a large dataset as Higgs Boson, collected from public site UCI and Higgs dataset downloaded from Kaggle site, in the experimentation stage.

关键词： Boson Higgs Spark Pyspark Machine Learning (ML) Logistic Regression (LR) Decision Tree (DT) Random Forest (RF) Gradient Boosted Tree (GBT) AUC Accuracy

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Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

arXiv

引用

arXiv 2020年

作者： Chen, Lei Wu, Le Hong, Richang Zhang, Kun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering HeFei University of Technology School of Computer Science and Technology University of Science and Technology of China

Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering (CF) based Recommender Systems (RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data. The proposed model is a linear model and it is easy to train, scale to large datasets, and yield better efficiency and effectiveness on two real datasets. We publish the source code at https://***/newlei/LRGCCF. Copyright © 2020, The Authors. All rights reserved.

关键词： Collaborative filtering

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data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

TechRxiv

引用

TechRxiv 2021年

作者： Zhang, Zhao Wei, Yanyan Zhang, Haijun Yang, Yi Yan, Shuicheng Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China Shenzhen China Centre for Artificial Intelligence University of Technology Sydney SydneyNSW Australia Department of Electrical and Computer Engineering National University of Singapore Singapore Hefei University of Technology China

Single Image Deraining task aims at recovering the rain-free background from an image degraded by rain streaks and rain accumulation. For the powerful fitting ability of deep neural networks and massive training data, data-driven deep SID methods obtained significant improvement over traditional ones. Current SID methods usually focus on improving the deraining performance by proposing different kinds of deraining networks, while neglecting the interpretation of the solving process. As a result, the generalization ability may still be limited in real-world scenarios, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (e.g., object detection). To explore these issues, we in this paper re-examine the three important factors (i.e., data, rain model and network architecture) for the SID problem, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We also study the relationship of the three factors from a new perspective of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. We further discuss the current mainstream data-driven SID methods from five aspects, i.e., training strategy, network pipeline, domain knowledge, data preprocessing, and objective function, and some useful conclusions are summarized by statistics. Besides, we profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets through extensive experiments to reveal the effectiveness of SID data. Finally, with the comprehensive review and in-depth analysis, we draw some valuable conclusions and suggestions for future research. © 2021, CC BY.

关键词： Object detection

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