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Crowdfunding dynamics tracking: A reinforcement learning approach

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arXiv 2019年

作者： Wang, Jun Zhang, Hefu Liu, Qi Pan, Zhen Tao, Hanqing Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China

Recent years have witnessed the increasing interests in research of crowdfunding mechanism. In this area, dynamics tracking is a significant issue but is still under exploration. Existing studies either fit the fluctuations of time-series or employ regularization terms to constrain learned tendencies. However, few of them take into account the inherent decision-making process between investors and crowdfunding dynamics. To address the problem, in this paper, we propose a Trajectory-based Continuous Control for Crowdfunding (TC3) algorithm to predict the funding progress in crowdfunding. Specifically, actor-critic frameworks are employed to model the relationship between investors and campaigns, where all of the investors are viewed as an agent that could interact with the environment derived from the real dynamics of campaigns. Then, to further explore the in-depth implications of patterns (i.e., typical characters) in funding series, we propose to subdivide them into fast-growing and slow-growing ones. Moreover, for the purpose of switching from different kinds of patterns, the actor component of TC3 is extended with a structure of options, which comes to the TC3-Options. Finally, extensive experiments on the Indiegogo dataset not only demonstrate the effectiveness of our methods, but also validate our assumption that the entire pattern learned by TC3-Options is indeed the U-shaped one. Copyright © 2019, The Authors. All rights reserved.

关键词： Crowdsourcing

Rumor Detection with Hierarchical Recurrent Convolutional Neural Network 8th

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Rumor Detection with Hierarchical Recurrent Convolutional Ne...

8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Lin, Xiang Liao, Xiangwen Xu, Tong Pian, Wenjing Wong, Kam-Fai College of Mathematics and Computer Science Fuzhou University Fuzhou China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei China The Chinese University of Hong Kong Sha Tin Hong Kong

ISBN: (纸本)9783030322359

Automatic rumor detection for events on online social media has attracted considerable attention in recent years. Usually, the events on social media are divided into several time segments, and for each segment, corresponding text will be converted as vectors for various neural network models to detect rumors. During this process, however, only sentence-level embedding has been considered, while the contextual information at the word level has been largely ignored. To address that issue, in this paper, we propose a novel rumor detection method based on a hierarchical recurrent convolutional neural network, which integrates contextual information for rumor detection. Specifically, with dividing events on social media into time segments, recurrent convolution neural network is adapted to learn the contextual representation information. Along this line, a bidirectional GRU network with attention mechanism is integrated to learn the time period information via combining event feature vectors. Experiments on real-world data sets validate that our solution could outperform several state-of-the-art methods. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

Learning the compositional visual coherence for complementary recommendations 29

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Learning the compositional visual coherence for complementar...

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Li, Zhi Wu, Bo Liu, Qi Wu, Likang Zhao, Hongke Mei, Tao Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science University of Science and Technology of China China Columbia University United States School of Computer Science and Technology University of Science and Technology of China China College of Management and Economics Tianjin University China JD AI Research

ISBN: (纸本)9780999241165

Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. Existing work mainly focused on modeling the co-purchased relations between two items, but the compositional associations of item collections are largely unexplored. Actually, when a user chooses the complementary items for the purchased products, it is intuitive that she will consider the visual semantic coherence (such as color collocations, texture compatibilities) in addition to global impressions. Towards this end, in this paper, we propose a novel Content Attentive Neural Networks (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents. Specifically, we first propose a Global Coherence Learning (GCL) module based on multi-heads attention to model the global compositional coherence. Then, we generate the semantic-focal representations from different semantic regions and design a Focal Coherence Learning (FCL) module to learn the focal compositional coherence from different semantic-focal representations. Finally, we optimize the CANN in a novel compositional optimization strategy. Extensive experiments on the large-scale real-world data clearly demonstrate the effectiveness of CANN compared with several state-of-the-art methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Semantics

ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

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arXiv 2020年

作者： Hao, Zhongkai Lu, Chengqiang Huang, Zhenya Wang, Hao Hu, Zheyuan Liu, Qi Chen, Enhong Lee, Cheekong Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China Tencent America

Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of scarce labeled molecules in the chemical space, where such property labels are generally obtained by Density Functional Theory (DFT) calculation which is extremely computational costly. An effective solution is to incorporate the unlabeled molecules in a semi-supervised fashion. However, learning semi-supervised representation for large amounts of molecules is challenging, including the joint representation issue of both molecular essence and structure, the conflict between representation and property leaning. Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules. Specifically, ASGN adopts a teacher-student framework. In the teacher model, we propose a novel semi-supervised learning method to learn general representation that jointly exploits information from molecular structure and molecular distribution. Then in the student model, we target at property prediction task to deal with the learning loss conflict. At last, we proposed a novel active learning strategy in terms of molecular diversities to select informative data during the whole framework learning. We conduct extensive experiments on several public datasets. Experimental results show the remarkable performance of our ASGN framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Density functional theory

MCNE: An end-to-end framework for learning multiple conditional network representations of social network

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arXiv 2019年

作者： Wang, Hao Xu, Tong Liu, Qi Lian, Defu Chen, Enhong Du, Dongfang Wu, Han Su, Wen Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Tencent Inc China

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention. Though large efforts have been made, they may fail to describe the multiple aspects of similarity between social users, as only a single vector for one unique aspect has been represented for each node. To that end, in this paper, we propose a novel end-to-end framework named MCNE to learn multiple conditional network representations, so that various preferences for multiple behaviors could be fully captured. Specifically, we first design a binary mask layer to divide the single vector as conditional embeddings for multiple behaviors. Then, we introduce the attention network to model interaction relationship among multiple preferences, and further utilize the adapted message sending and receiving operation of graph neural network, so that multi-aspect preference information from high-order neighbors will be captured. Finally, we utilize Bayesian Personalized Ranking loss function to learn the preference similarity on each behavior, and jointly learn multiple conditional node embeddings via multi-task learning framework. Extensive experiments on public datasets validate that our MCNE framework could significantly outperform several state-of-the-art baselines, and further support the visualization and transfer learning tasks with excellent interpretability and robustness. Copyright © 2019, The Authors. All rights reserved.

关键词： Network embeddings

Winning the CVPR'2022 AQTC Challenge: A Two-stage Function-centric Approach

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arXiv 2022年

作者： Wu, Shiwei He, Weidong Xu, Tong Wang, Hao Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence University of Science and Technology of China China

Affordance-centric Question-driven Task Completion for Egocentric Assistant (AQTC) is a novel task which helps AI assistant learn from instructional videos and scripts and guide the user step-by-step. In this paper, we deal with the AQTC via a two-stage Function-centric approach, which consists of Question2Function Module to ground the question with the related function and Function2Answer Module to predict the action based on the historical steps. We evaluated several possible solutions in each module and obtained significant gains compared to the given baselines. Our code is available at https://***/starsholic/LOVEU-CVPR22-AQTC. © 2022, CC BY-NC-ND.

关键词：

User Rating Prediction Based on Trust-Driven Probabilistic Matrix Factorization

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Ruan Jian Xue Bao/Journal of Software 2018年第12期29卷 3747-3763页

作者： Du, Dong-Fang Xu, Tong Lu, Ya-Nan Guan, Chu Liu, Qi Chen, En-Hong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei230027 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China

The development of Internet has brought convenience to the public, but also troubles users in making choices among enormous data. Thus, recommender systems based on user understanding are urgently in need. Different from the traditional techniques that usually focus on individual users, the social-based recommender systems perform better with integrating social influence modeling to achieve more accurate user profiling. However, current works usually generalize influence in simple mode, while deep discussions on intrinsic mechanism have been largely ignored. To solve this problem, this paper studies the social influence within users who affects both rating and user attributes, and then proposes a novel trust-driven PMF (TPMF) algorithm to merge these two mechanisms. Furthermore, to deal with the task that different user should have personalized parameters, the study clusters users according to rating correlation and then maps them to corresponding weights, thereby achieving the personalized selection of users' model parameters. Comprehensive experiments on open data sets validate that TPMF and its derivation algorithm can effectively predict users' rating compared with several state of the art baselines, which demonstrates the capability of the presented influence mechanism and technical framework. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Recommender systems

Technical Phrase Extraction for Patent Mining: A Multi-level Approach

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Technical Phrase Extraction for Patent Mining: A Multi-level...

IEEE International Conference on data Mining (ICDM)

作者： Ye Liu Han Wu Zhenya Huang Hao Wang Jianhui Ma Qi Liu Enhong Chen Hanqing Tao Ke Rui Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science & School of Computer Science and Technology University of Science and Technology of China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Recent years have witnessed a booming increase of patent applications, which provides an open chance for revealing the inner law of innovation, but in the meantime, puts forward higher requirements on patent mining techniques. Considering that patent mining highly relies on patent document analysis, this paper makes a focused study on constructing a technology portrait for each patent, i.e., to recognize technical phrases concerned in it, which can summarize and represent patents from a technology angle. To this end, we first give a clear and detailed description about technical phrases in patents based on various prior works and analyses. Then, combining characteristics of technical phrases and multi-level structures of patent documents, we develop an Unsupervised Multi-level Technical Phrase Extraction (UMTPE) model. Particularly, a novel evaluation metric called Information Retrieval Efficiency (IRE) is designed to evaluate the extracted phrases from a new perspective, which greatly supplements traditional metrics like Precision and Recall. Finally, extensive experiments on real-world patent data show the effectiveness of our UMTPE model.

关键词： Measurement Patents Technological innovation Text analysis Conferences Information retrieval data models

Chinese embedding via stroke and glyph information: A dual-channel view

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arXiv 2019年

作者： Taoy, Hanqing Tongy, Shiwei Xuyx, Tong Liuyx, Qi Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China School of Data Science University of Science and Technology of China

Recent studies have consistently given positive hints that morphology is helpful in enriching word embeddings. In this paper, we argue that Chinese word embeddings can be substantially enriched by the morphological information hidden in characters which is reflected not only in strokes order sequentially, but also in character glyphs spatially. Then, we propose a novel Dual-channel Word Embedding (DWE) model to realize the joint learning of sequential and spatial information of characters. Through the evaluation on both word similarity and word analogy tasks, our model shows its rationality and superiority in modelling the morphology of Chinese. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings