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A survey on causal inference for recommendation

The Innovation 2024年第2期5卷 130-144页

作者： Huishi Luo Fuzhen Zhuang Ruobing Xie Hengshu Zhu Deqing Wang Zhulin An Yongjun Xu Institute of Artificial Intelligence Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100094China WeChat Search Application Department TencentBeijing 100080China The Career Science Laboratory BOSS ZhipinBeijing 100028China sklsde School of Computer ScienceBeihang UniversityBeijing 100191China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Causal inference has recently garnered significant interest among recommender system(RS)researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple *** offers a framework to model the causality in RSs such as confounding effects and deal with counterfactual problems such as offline policy evaluation and data *** there are already some valuable surveys on causal recommendations,they typically classify approaches based on the practical issues faced in RS,a classification that may disperse and fragment the uni-fied causal *** RS researchers’unfamiliarity with causality,it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective,thereby facilitating a deeper integration of causal inference in *** survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal ***,we introduce the fundamental concepts of causal inference as the basis of the following ***,we propose a novel theory-driven taxonomy,categorizing existing methods based on the causal theory employed,namely those based on the potential outcome framework,the structural causal model,and general *** review then delves into the technical details of how existing methods apply causal inference to address particular recommender ***,we highlight some promising directions for future research in this *** papers and open-source resources will be progressively available at https://***/Chrissie-Law/Causal-Inference-forRecommendation.

关键词： survey details causal

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Mining Typical Treatment Duration Patterns for Rational Drug Use from Electronic Medical Records

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Journal of Systems Science and Systems Engineering 2019年第5期28卷 602-620页

作者： Jingfeng Chen Chonghui Guo Leilei Sun Menglin Lu Institute of Systems Engineering Dalian University of Technology Dalian 116024 China sklsde and BDBC Lab Beihang University Beijing 100083 China

Rational drug use requires that patients receive medications for an adequate period of *** adequate duration time of medications not only improve the therapeutic effect of medicines,but also reduce the side effects and adverse reactions of *** paper proposes a data-driven method to mine typical treatment duration patterns for rational drug use from electronic medical records (EMRs).Firstly,a quintuple is defined to describe drug use duration statistics (DUDS) for each drug and treatment record is further represented with DUDS vector (DUDSV).Next a similarity measure method is adopted to compute the similarity between treatment ***,a clustering algorithm is used to cluster all patient treatment records to extract typical treatment duration patterns including typical drug sets,effective drug use day sets,and the DUDSs of each typical *** the extracted typical treatment duration patterns are evaluated and annotated based on patients' demographic information,disease severity scores,treatment outcome and diagnostic ***,a real-world EMR dataset is performed to indicate that the approach we proposed can effectively mine typical treatment duration patterns from EMRs and recommend the appropriate treatment regimens for patients based on their admission information.

关键词： EMR data mining rational drug use typical treatment duration pattern similarity measure clustering

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A Simulation Research Towards Better Leverage of Sales Ranking

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Journal of Systems Science and Systems Engineering 2021年第1期30卷 105-122页

作者： Lin Tang Leilei Sun Chonghui Guo Yuqian Zuo Zhen Zhang Institute of Systems Engineering Dalian University of TechnologyDalian116024China sklsde and BDBC Lab Beihang UniversityBeijing100090China

As a kind of the most significantly popular information in markets,the sales ranking has great impacts on consumer ***,there are few discussions on how sales ranking should be provided to consumers in the *** paper aims to answer the following two questions:1)To what extent does the sales ranking influence consumer choices;2)When the sales ranking should be provided to *** do so,this paper first constructs a sales ranking model and then provides detailed simulation experiments to demonstrate the *** experimental results show that for markets where consumer preferences are dramatically different,such as music and movie markets,sales rankings do not have significant influences on consumer choices and should not be provided to consumers until a large number of early independent consumer choices have been *** for markets in which consumer preferences are similar,such as markets for official supplies,sales rankings have more influences on consumer choices and should be provided to consumers ***,an evolution strategy is proposed to ascertain the most suitable sales rankings(characterised by suitable influence strength and suitable release time)for some specified online *** comparison results show that the optimized sales rankings not only can help consumers discover higher-quality products but also can improve overall sales.

关键词： Marketing sales ranking popularity information simulation experiments

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TOPIC SPLITTING： A HIERARCHICAL TOPIC MODEL BASED ON NON-NEGATIVE MATRIX FACTORIZATION

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Journal of Systems Science and Systems Engineering 2018年第4期27卷 479-496页

作者： Rui Liu Xingguang Wang Deqing Wang Yuan Zuo He Zhang Xianzhu Zheng sklsde School of Computer Science Beihang University Beijing 100191 China Department of Scientific Research Academy of Armored Force Engineering Beijing 100072 China

Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics＇ quality. Most of traditional methods build a hierarchy by adopting low-level topics as new features to construct high-level ones, which will often cause semantic confusion between low-level topics and high-level ones. To address the above problem, we propose a novel topic model named hierarchical sparse NMF with orthogonal constraint （HSOC）, which is based on non-negative matrix factorization and builds topic hierarchy via splitting super-topics into sub-topics. In HSOC, we introduce global independence, local independence and information consistency to constraint the split topics. Extensive experimental results on real-world corpora show that the purposed model achieves comparable performance on topic quality and better performance on semantic feature representation of documents compared with baseline methods.

关键词： Hierarchical topic model non-negative matrix factorization hierarchical NMF topic splitting

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Non-salient region erasure for time series augmentation

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Frontiers of Computer Science 2022年第6期16卷 203-205页

作者： Pin LIU Xiaohui GUO Bin SHI Rui WANG Tianyu WO Xudong LIU State Key Lab of Software Development Environment(sklsde) Beihang UniversityBeijing100191China Beijing Advanced Innovation Center for Big Data and Brain Computing(BDBC) Beijing100191China Hangzhou Innovation Institute Beihang UniversityHangzhou310052China School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an710049China

1 Introduction Time seriesaugmentationis an essential approachto solvethe overfitting problem on the time series classification(TSC)task[1,2].Although existing approaches perform better in mitigating this problem,none of them focus on protecting saliency regions on time *** key informative shapelets contained in these regions are the core basis for distinguishing categories(e.g.,upward spikes in ECG and high amplitude in Sensor).

关键词： Sensor series series

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Real-time bottleneck matching in spatial crowdsourcing

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Science China(Information Sciences) 2021年第8期64卷 233-234页

作者： Long LI Lingling WANG Weifeng LV State Key Laboratory of Software Development Environment (sklsde Lab) and Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University School of Management and Economics Beijing Institute of Technology

Dear editor,Spatial crowdsourcing(SC) services(e.g., Uber, DiDi, and Meituan) have become popular with smart-phone ***, the online matching problems in real-time spatial data are a key issue in SC [1–4]. Unlike the current one-sided online matching study in real-time spatial data [5], which focuses on minimizing the overall cost of the matching,

关键词： Real-time bottleneck matching in spatial crowdsourcing

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Foreword to the Special Issue

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Journal of Systems Science & Complexity 2019年第1期32卷 1-2页

作者： GAO Xiao-Shan LI Hongbo WANG Dongming Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science University of Chinese Academy of Sciences Chinese Academy of Sciences LMIB–sklsde–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Centre National de la Recherche Scientifique

On May 12,2019 we shall celebrate the centenary birthday of the late Professor Wu Wen-Tsun (1919-2017),one of the most famous and influential mathematicians in *** made foundational contributions to the field of topology and established Mathematics Mechanization as a *** devoted himself to an academic career of more than six decades with leadership and extensive activities of research and education across mathematics to computer science and artificial *** scope of his research spans from algebraic topology, differential topology,and algebraic geometry to automated reasoning,symbolic computation, and game theory,and to the history of *** early work in topology was a major breakthrough,leading to well-known and now classical results including the characteristic class and formulas named after *** the late 1970s,he pioneered the research of Mathematics Mechanization through his invention of the "Wu method",which revolutionized the field of automated reasoning.

关键词： shall celebrate centenary birthday established Mathematics

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Characteristic Decomposition: From Regular Sets to Normal Sets

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Journal of Systems Science & Complexity 2019年第1期32卷 37-46页

作者： MOU Chenqi WANG Dongming LMIB–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University LMIB–sklsde–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Centre National de la Recherche Scientifique

In this paper it is shown how to transform a regular triangular set into a normal triangular set by computing the W-characteristic set of their saturated ideal and an algorithm is proposed for decomposing any polynomial set into ?nitely many strong characteristic pairs, each of which is formed with the reduced lexicographic Gr?bner basis and the normal W-characteristic set of a characterizable ideal.

关键词： Characteristic decomposition characteristic pair normal set regular set W-characteristic set

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Crysformer:An attention-based graph neural network for properties prediction of crystals

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中国物理B:英文版 2023年第9期32卷 15-20页

作者：王田陈家辉滕婧史金钢曾新华 Hichem Snoussi Institute of Artificial Intelligence SKLSDEBeihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100191China School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Software Engineering Xi'an Jiaotong UniversityXi'an 710049China Academy for Engineering and Technology Fudan UniversityShanghai 200433China Charles Delaunay Institute University of Technology of TroyesTroyes Cedex 10004France

We present a novel approach for the prediction of crystal material properties that is distinct from the computationally complex and expensive density functional theory(DFT)-based ***,we utilize an attention-based graph neural network that yields high-accuracy *** approach employs two attention mechanisms that allow for message passing on the crystal graphs,which in turn enable the model to selectively attend to pertinent atoms and their local environments,thereby improving *** conduct comprehensive experiments to validate our approach,which demonstrates that our method surpasses existing methods in terms of predictive *** results suggest that deep learning,particularly attention-based networks,holds significant promise for predicting crystal material properties,with implications for material discovery and the refined intelligent systems.

关键词： deep learning property prediction crystal attention networks

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Find truth in the hands of the few:acquiring specific knowledge with crowdsourcing

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Frontiers of Computer Science 2021年第4期15卷 5-16页

作者： Tao HAN Hailong SUN Yangqiu SONG Yili FANG Xudong LIU sklsde Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyClearwater BayHong Kong 999077China School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful ***,when we aggregate the crowd knowledge based on the currently developed voting algorithms,it often results in common knowledge that may not be *** this paper,we consider the problem of collecting specific knowledge via *** the help of using external knowledge base such as WordNet,we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more *** formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption,and solve it by the expectation-maximization(EM)*** increase algorithm compatibility,we also refine our method into semi-supervised *** results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected,especially for sparse data.

关键词： crowdsourcing knowledge acquisition EM algorithm label aggregation

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