Computational Behavioral Modeling for Big User Data

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About this Research Topic

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Background

With massive amounts of data currently available and being collected, obtaining access to data is seldom the concern. Information is being produced and stored at an unprecedented rate, and increasingly, much of the big data being collected is about human behavior. Our behavior is captured in the information that we provide from using web search engines, e-commercial platforms, social network services, or online education. Sifting through this data and deriving insights on human behavior enables the platforms to make more effective decisions and provide better service. However, traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. There is a great need for computational models for tasks such as pattern analysis, prediction, recommendation, and anomaly detection, on large scale datasets.

This research topic reports innovative solutions to problems of user behavior data scale in a wide range of applications such as recommender systems and suspicious behavior detection. It covers data science and statistical approaches to knowledge discovery and modeling, decision support and prediction, including machine learning and AI, on user behavior data. Potential contexts include i) modeling underlying factors such as personal preference and interpersonal influence of a large population; ii) modeling large user data across multiple domains and/or platforms; iii) scalable computational methods including large-scale networked data analysis, matrix and tensor data analysis, graph learning, transfer learning, few-shot learning, and meta learning methods for capturing behavioral patterns in large user data.

In this Research Topic we invite contributions that introduce computational methods and big data technologies for user behavior modeling that help to develop a better understanding of many aspects of human behaviors (such as intention, preference, influence, spatiotemporal pattern) or demonstrate a novel application of these technologies to a particular domain. Potential topics include, but are not restricted to:

• Text mining for modeling personal preference
• Social network analysis for modeling interpersonal influence
• Advanced NLP techniques for modeling behavioral content
• Multi-dimensional data mining for modeling spatiotemporal patterns
• Modeling cross-domain and cross-platform behaviors
• Modeling a variety of behavioral intentions

• Networked data analysis for behavior modeling
• Matrix and tensor data analysis for behavior modeling
• Graph learning techniques for behavior modeling
• Transfer learning methods for behavior modeling
• Meta-learning and few-shot learning for behavior modeling

• Scalable methods for recommender systems
• Deep learning methods for recommender systems
• Multimodal learning methods for recommender systems
• Data-driven methods for detecting misinformation and misbehavior
• New applications in suspicious behavior detection

Topic editor Neil Shah is employed by Snap. All other Topic Editors declare no competing interests with regards to the Research Topic subject.


Keywords: Recommender Systems, Misbehavior Detection, Social Network Analysis, Graph Learning, Deep Learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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