Behavior Informatics: A New Perspective

@article{Cao2014BehaviorIA,
  title={Behavior Informatics: A New Perspective},
  author={L. Cao and T. Joachims and Can Wang and {\'E}ric Gaussier and Jinjiu Li and Y. Ou and D. Luo and R. Zafarani and Huan Liu and Guandong Xu and Zhiang Wu and G. Pasi and Ya Zhang and Xiaokang Yang and H. Zha and Edoardo Serra and V. S. Subrahmanian},
  journal={IEEE Intelligent Systems},
  year={2014},
  volume={29},
  pages={62-80}
}
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… Expand
Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management
TLDR
This talk introduces some of real-life applications of behavior informatics in core business, capital markets and government services, associated with highly significant economic benefits and social impact as a result of applying the resultant behavior insight and behavior intelligence. Expand
A survey for user behavior analysis based on machine learning techniques: current models and applications
TLDR
This paper aims to thoroughly analyze the existing references, to promote the dissemination of state-of-the-art approaches discussing their strong and weak points, and to identify open challenges and prospective future research directions. Expand
Health and medical behavior informatics
Abstract Behavior matters, and behavior informatics is the approach to discover and apply behavior intelligence and value for behavior-related problem-solving and interventions. Health behaviorExpand
Behavior Analysis for Electronic Commerce Trading Systems: A Survey
TLDR
This paper focuses on analyzing both system behavior and user behavior in e-commerce trading systems, and divides user behaviors into four groups: web browsing behavior, keystroke behavior, network transaction behavior, and mobile terminal behavior. Expand
CompetitiveBike : Competitive Analysis and Popularity Prediction of Bike-Sharing Apps Leveraging Multi-Source Data
In recent years, bike-sharing systems have been widely deployed in many big cities, which provide an economical lifestyle. With the prevalence of bike-sharing systems, a lot of companies join theExpand
An expressive event-based language for representing user behavior patterns
TLDR
This paper defines a semantic model of the user behavior and presents syntax and semantics of a generic Behavior Pattern Language (BPL), which enables the analysts to define a variety of complex behavior patterns in a declarative manner. Expand
Discovering behavioral profiles for website visitors of higher educations
TLDR
This paper focuses on identifying behavioural profiles to increase customer base and increase conversion rate, and reveals three behavioural profiles for the website visitors of the University of Twente by utilizing the framework and the model proposed in this paper. Expand
CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data
TLDR
This paper develops CompetitiveBike, a system to predict the popularity contest among bike-sharing apps in China, and conducts experiments to demonstrate the effectiveness of the approach. Expand
An interactive human centered data science approach towards crime pattern analysis
TLDR
The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. Expand
Detecting suspicious entities in Offshore Leaks networks
TLDR
This paper proposes a new ranking algorithm, named Suspiciousness Rank Back and Forth (SRBF), that, given one of the networks in the ICIJ Offshore Leaks Database, leverages the network structure and the blacklist ground truth to assign a degree of suspiciousness to each entity in the network. Expand
...
1
2
...

References

SHOWING 1-10 OF 23 REFERENCES
In-depth behavior understanding and use: The behavior informatics approach
  • L. Cao
  • Computer Science
  • Inf. Sci.
  • 2010
TLDR
The approach of behavior informatics is proposed, 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. Expand
Behavior Computing
TLDR
Researchers, research students and practitioners in behavior studies, including computer science, behavioral science, and social science communities will find this state of the art volume invaluable. Expand
Modeling and Analysis of Social Activity Process
Behavior modeling has been increasingly recognized as a crucial means for disclosing interior driving forces and impact in social activity processes. Tra- ditional behavior modeling in behavior andExpand
Social Media Mining: An Introduction
TLDR
Social Media Mining introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Expand
Coupled Behavior Analysis with Applications
TLDR
A Coupled Hidden Markov Model (CHMM)-based approach is illustrated to model and detect abnormal group-based trading behaviors and it is demonstrated that the proposed CHMMs outperforms HMM-only for modeling a single sequence or combining multiple single sequences, without considering coupling relationships to detect anomalies. Expand
Connecting users across social media sites: a behavioral-modeling approach
TLDR
This study formally defines the cross-media user identification problem, introduces a methodology (MOBIUS) for finding a mapping among identities of individuals across social media sites, and shows that MOBIUS is effective in identifying users across socialMedia sites. Expand
Group Recommendation: Semantics and Efficiency
TLDR
This paper analyzes the desiderata of group recommendation and proposes a formal semantics that accounts for both item relevance to a group and disagreements among group members and designs and implements algorithms for efficiently computing group recommendations. Expand
The Turn - Integration of Information Seeking and Retrieval in Context
TLDR
The Turn represents a wide-ranging perspective of IS&R by providing a novel unique research framework, covering both individual and social aspects of information behavior, including the generation, searching, retrieval and use of information. Expand
Collaborative filtering with social regularization for TV program recommendation
TLDR
The experimental results show that the proposed algorithm significantly outperforms the state-of-the-art collaborative filtering method, demonstrating the importance of incorporating social trust and item similarity in recommendation. Expand
Group formation in large social networks: membership, growth, and evolution
TLDR
It is found that the propensity of individuals to join communities, and of communities to grow rapidly, depends in subtle ways on the underlying network structure, and decision-tree techniques are used to identify the most significant structural determinants of these properties. Expand
...
1
2
3
...