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23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Cao, Longbing Univ Technol Sydney Adv Analyt Inst Sydney NSW Australia

ISBN: (纸本)9781450348874

behavior is ubiquitous, and behavior intelligence and insight play an important role in data understanding and business problem-solving. behavior informatics [1,2] emerges as an important tool for discovering behavior intelligence and behavior insight. As a computational concept, behavior captures the aspects of the demographics of behavioral subjects and objects; social relationships or norms governing the interactions between behaviors of an individual or a group; behavior sequences or networks and their dynamics; and the impact or effect generated by the behaviors undertaken by subjects on objects. Accordingly, a behavior model [2] captures the subject and the object of a behavior or behavior sequence, the activities conducted by its subject on objects, and the relationships between activities; behavior subject, object, activities and relationships are characterized by their respective attributes. As a result, a behavior is represented as a behavior attributes-based vector; and a subject's behaviors at a time period form a vector-based sequence, namely, represented as a behavior attribute vector-based matrix [3]. With such behavior modeling and from the informatics perspective, behavior informatics takes a top-down approach to systematically and deeply represent, model, reason about, and aggregate behaviors [4]; and a bottom-up approach to analyze and learn behavior occurrences, non-occurrences, dynamics, impact, and utility [2].Accordingly, for a real-life problem, first, its data is converted to behavioral data according to the above behavior model, characterized by the relevant activities that form behavior sequences, and the properties of subjects, objects, activities, and relationships. Second, analytical tasks, such as behavior pattern analysis, abnormal behavior detection, coupled analysis of group behaviors, modeling of behavior impact and utility, discovery of high impact and high utility behaviors, analysis of non-occurring behaviors, and analysis of be

关键词： behavior informatics behavior computing behavior intelligence behavior insight behavior modeling user modeling coupled behavior analysis non-occurring behavior

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behavior informatics: A New Perspective

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IEEE INTELLIGENT SYSTEMS 2014年第4期29卷 62-66页

作者： Cao, Longbing Univ Technol Sydney Adv Analyt Inst Sydney NSW 2007 Australia Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia

This installment of Trends & Controversies provides an array of perspectives on the latest research in behavior informatics. Longbing Cao introduces the work in "behavior informatics: A New Perspective." Then, in "behavior Computing," Longbing Cao and Thorsten Joachims provide a basic overview of the topic. Next is "Coupled behavior Representation, Modeling, Analysis, and Reasoning" by Can Wang, Longbing Cao, Eric Gaussier, Jinjiu Li, Yuming Ou, and Dan Luo. The fourth article is "behavior Analysis in Social Media," by Reza Zafarani and Huan Liu. The fifth article is "Group Recommendation and behavior," by Guandong Xu and Zhiang Wu. Gabriella Pasi wrote the sixth article, "Web Search and behavior." The seventh article, "behaviors of IPTV Users," is by Ya Zhang, Xiaokang Yang, and Hongyuan Zha. Finally, "Should behavioral Models of Terror Groups Be Disclosed?" is by Edoardo Serra and V.S. Subrahmanian.

关键词： behavioural sciences computing behavior computing behavior informatics Analytical models behavioral science Big data Business Computational modeling Data mining informatics Big Data behavior analysis behavior analytics behavior computing behavior informatics coupled behavior group behavior group profile group recommender systems intelligent systems social networking

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behavioral Data Categorization for Transformers-based Models in Digital Health

Behavioral Data Categorization for Transformers-based Models...

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4th IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) / 18th IEEE-EMBS International Conference on Biomedical and Health informatics (BHI)

作者： Siebra, Clauirton Matias, Igor Wac, Katarzyna Univ Geneva Qual Life Technol Lab Geneva Switzerland

ISBN: (数字)9781665487917

ISBN: (纸本)9781665487917

Transformers are recent deep learning (DL) models used to capture the dependence between parts of sequential data. While their potential was already demonstrated in the natural language processing (NLP) domain, emerging research shows transformers can also be an adequate modeling approach to relate longitudinal multi-featured continuous behavioral data to future health outcomes. As transformers-based predictions are based on a domain lexicon, the use of categories, commonly used in specialized areas to cluster values, is the likely way to compose lexica. However, the number of categories may influence the transformer prediction accuracy, mainly when the categorization process creates imbalanced datasets, or the search space is very restricted to generate optimal feasible solutions. This paper analyzes the relationship between models' accuracy and the sparsity of behavioral data categories that compose the lexicon. This analysis relies on a case example that uses mQoL-Transformer to model the influence of physical activity behavior on sleep health. Results show that the number of categories shall be treated as a further transformer's hyperparameter, which can balance the literature-based categorization and optimization aspects. Thus, DL processes could also obtain similar accuracies compared to traditional approaches, such as long short-term memory, when used to process short behavioral data sequences.

关键词： deep learning transformers human behavior recommendations behavior informatics digital biomarkers

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VM-NSP: Vertical Negative Sequential Pattern Mining with Loose Negative Element Constraints

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2021年第2期39卷 p1-27页

作者： Wang, Wei Cao, Longbing Univ Technol Sydney Data Sci Lab Sydney NSW Australia

Negative sequential patterns (NSPs) capture more informative and actionable knowledge than classic positive sequential patterns (PSPs) due to the involvement of both occurring and nonoccurring behaviors and events, which can contribute to many relevant applications. However, NSP mining is nontrivial, as it involves fundamental challenges requiring distinct theoretical foundations and is not directly addressable by PSP mining. In the very limited research reported on NSP mining, a negative element constraint (NEC) is incorporated to only consider the NSPs composed of specific forms of elements (containing either positive or negative items), which results in many valuable NSPs being missed. Here, we loosen the NEC (called loose negative element constraint (LNEC)) to include partial negative elements containing both positive and negative items, which enables the discovery of more flexible patterns but incorporates significant new learning challenges, such as representing and mining complete NSPs. Accordingly, we formalize the LNEC-based NSP mining problem and propose a novel vertical NSP mining framework, VM-NSP, to efficiently mine the complete set of NSPs by a vertical representation (VR) of each sequence. An efficient bitmap-based vertical NSP mining algorithm, bM-NSP, introduces a bitmap hash table-based VR and a prefix-based negative sequential candidate generation strategy to optimize the discovery performance. VM-NSP and its implementation bM-NSP form the first VR-based approach for complete NSP mining with LNEC. Theoretical analyses and experiments confirm the performance superiority of bM-NSP on synthetic and real-life datasets w.r.t. diverse data factors, which substantially expands existing NSP mining methods toward flexible NSP discovery.

关键词： Sequence analysis negative sequence analysis negative sequential pattern mining vertical representation nonoccurring behavior analytics behavior informatics

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Uncovering and Predicting Human behaviors

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IEEE INTELLIGENT SYSTEMS 2016年第2期31卷 77-78页

作者： Cui, Peng Liu, Huan Aggarwal, Charu Wang, Fei Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Arizona State Univ Tempe AZ 85287 USA IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Univ Connecticut Storrs CT 06269 USA

This installment of Trends & Controversies provides an array of perspectives on the latest research in modeling user behavior. Peng Cui, Huan Liu, Charu Aggarwal, and Fei Wang introduce the field in "Uncovering and Predicting Human behaviors." The essays included are "Computational Modeling of Complex User behaviors: Challenges and Opportunities," by Peng Cui, Huan Liu, Charu Aggarwal, and Fei Wang; "Non-IID Recommendation Theories and Systems," by Longbing Cao and Philip S. Yu; "User behavior Modeling and Fraud Detection," by Alex Beutel and Christos Faloutsos; and "Transfer Learning for behavior Prediction," by Weike Pan and Qiang Yang.

关键词： Computational Modeling Social Network Services Context Modeling Data Mining Analytical Models Market Research Predictive Models Intelligent Systems Recommender Systems Modeling Human behavior behavior informatics Non II Dness Learning

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e-NSP: Efficient negative sequential pattern mining

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ARTIFICIAL INTELLIGENCE 2016年 235卷 156-182页

作者： Cao, Longbing Dong, Xiangjun Zheng, Zhigang Univ Technol Sydney Sydney NSW 2007 Australia Qilu Univ Technol Jinan Peoples R China

As an important tool for behavior informatics, negative sequential patterns (NSP) (such as missing medical treatments) are critical and sometimes much more informative than positive sequential patterns (PSP) (e.g. using a medical service) in many intelligent systems and applications such as intelligent transport systems, healthcare and risk management, as they often involve non-occurring but interesting behaviors. However, discovering NSP is much more difficult than identifying PSP due to the significant problem complexity caused by non-occurring elements, high computational cost and huge search space in calculating negative sequential candidates (NSC). So far, the problem has not been formalized well, and very few approaches have been proposed to mine for specific types of NSP, which rely on database re-scans after identifying PSP in order to calculate the NSC supports. This has been shown to be very inefficient or even impractical, since the NSC search space is usually huge. This paper proposes a very innovative and efficient theoretical framework: set theory-based NSP mining (ST-NSP), and a corresponding algorithm, e-NSP, to efficiently identify NSP by involving only the identified PSP, without re-scanning the database. Accordingly, negative containment is first defined to determine whether a data sequence contains a negative sequence based on set theory. Second, an efficient approach is proposed to convert the negative containment problem to a positive containment problem. The NSC supports are then calculated based only on the corresponding PSP. This not only avoids the need for additional database scans, but also enables the use of existing PSP mining algorithms to mine for NSP. Finally, a simple but efficient strategy is proposed to generate NSC. Theoretical analyses show that e-NSP performs particularly well on datasets with a small number of elements in a sequence, a large number of itemsets and low minimum support. e-NSP is compared with two currently avail

关键词： Negative sequence analysis Sequence analysis behavior analytics Non-occurring behavior behavior informatics behavior computing Pattern mining

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In-depth behavior understanding and use: The behavior informatics approach

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INFORMATION SCIENCES 2010年第17期180卷 3067-3085页

作者： Cao, Longbing Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia

The in-depth analysis of human behavior has been increasingly recognized as a crucial means for disclosing interior driving forces, causes and impact on businesses in handling many challenging issues such as behavior modeling and analysis in virtual organizations, web community analysis, counter-terrorism and stopping crime. The modeling and analysis of behaviors in virtual organizations is an open area. Traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. On the other hand, so-called behavior analysis is actually based on human demographic and business usage data, such as churn prediction in the telecommunication industry, in which behavior-oriented elements are hidden in routinely collected transactional data. As a result, it is ineffective or even impossible to deeply scrutinize native behavior intention, lifecycle and impact on complex problems and business issues. In this paper, we propose the approach of behavior informatics (Bp, in order to support explicit and quantitative behavior involvement through a conversion from source data to behavioral data, and further conduct genuine analysis of behavior patterns and impacts. BI consists of key components including behavior representation, behavioral data construction, behavior impact analysis, behavior pattern analysis, behavior simulation, and behavior presentation and behavior use. We discuss the concepts of behavior and an abstract behavioral model, as well as the research tasks, process and theoretical underpinnings of BI. Two real-world case studies are demonstrated to illustrate the use of 131 in dealing with complex enterprise problems, namely analyzing exceptional market microstructure behavior for market surveillance and mining for high impact behavior patterns in social security data for governmental debt prevention. Substantial experiments have shown that BI has the potential to greatly complement the existing empirical and specific

关键词： informatics behavior analysis behavior informatics behavior computing Decision making

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Nonoccurring behavior Analytics: A New Area

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IEEE INTELLIGENT SYSTEMS 2015年第6期30卷 4-11页

作者： Cao, Longbing Yu, Philip S. Kumar, Vipin Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Univ Illinois Dept Comp Sci Chicago IL 60680 USA Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

Nonoccurring behaviors (NOBs) refer to those behaviors that should happen but do not take place for some reasons. They are widely seen in online, business, government, health, medical, scientific, and social data and applications. Very limited research has been undertaken to explore such behaviors owing to their invisibility and the significant challenges in analyzing NOBs. This article outlines the concept, intrinsic characteristics, significant challenges, main issues, research directions, and state-of-the-art work related to NOBs, followed by the prospects for and applications of NOB analytics.

关键词： behavioural sciences NOB analytics intrinsic characteristics nonoccurring behavior analytics Analytical models behavioral science Complexity theory Computational modeling Economics Intelligent systems behavior analysis behavior informatics intelligent systems negative behavior analysis negative sequence analysis nonoccurring behavior occurring behavior

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Non-IIDness Learning in behavioral and Social Data

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COMPUTER JOURNAL 2014年第9期57卷 1358-1370页

作者： Cao, Longbing Univ Technol Sydney Adv Analyt Inst Sydney NSW 2007 Australia

Most of the classic theoretical systems and tools in statistics, data mining and machine learning are built on the fundamental assumption of IIDness, which assumes the independence and identical distribution of underlying objects, attributes and/or values. However, complex behavioral and social problems often exhibit strong couplings and heterogeneity between values, attributes and objects (i.e., non-IIDness). This fundamentally challenges the IIDness-based learning methodologies and techniques. This paper presents a high-level overview of the needs, challenges and opportunities of non-IIDness learning for handling complex behavioral and social problems. By reviewing the nature and issues of classic IIDness-based algorithms in frequent pattern mining, clustering and classification to complex behavioral and social applications, concepts, structures, frameworks and exemplar techniques are discussed for non-IIDness learning. Case studies, related work and prospects of non-IIDness learning are presented. Non-IIDness learning is also a fundamental issue in big data analytics.

关键词： non-IIDness learning IIDness, IID data non-IID data coupling behavior informatics social informatics

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A Framework for Multi-Faceted Analytics of User behaviors in Social Networks

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JOURNAL OF INTERNET TECHNOLOGY 2014年第6期15卷 985-994页

作者： Zhao, Yun Wei van den Heuvel, Willem-Jan Ye, Xiaojun Tsinghua Univ Sch Software Beijing Peoples R China Tilburg Univ Sch Econ & Management NL-5000 LE Tilburg Netherlands

In this paper, we propose a framework for conveniently conducting multi-faceted behavior based analytics in social networks. The breadth and depth of social network behavior analytics could thus be dramatically improved. The "breadth" refers to the coverage of analytic perspectives catering to the various analytic needs of end users, and the "depth" refers to the multi-dimensional dynamics of social network evolution. A detailed analysis of Twitter was then presented illustrating (i) the multi-faceted relationship between users based on their co-joining categories and co-spreading tweets with three orthogonal dimensions of affect analyzed simultaneously, i.e., valence, activation, and intention;(ii) the semantic network of Twitter topics based on the relations of the users and the tweets marked;(iii) multi-faceted social dynamics, i.e., the rise and fall of the multiple dimensions over time.

关键词： Knowledge representation Social network Data analytics behavior informatics

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