Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103679
Title: Behavior-interior-aware user preference analysis based on social networks
Authors: Wang, Can
Bo, Tao
Zhao, Yun Wei
Chi, Chi-Hung
Lam, Kwok-Yan
Wang, Sen
Shu, Min
Keywords: Social Networks
DRNTU::Engineering::Computer science and engineering
Behavior Interiors
Issue Date: 2018
Source: Wang, C., Bo, T., Zhao, Y. W., Chi, C.-H., Lam, K.-Y., Wang, S., & Shu, M. (2018). Behavior-interior-aware user preference analysis based on social networks. Complexity, 2018, 7371209-. doi:10.1155/2018/7371209
Series/Report no.: Complexity
Abstract: There is a growing trend recently in big data analysis that focuses on behavior interiors, which concern the semantic meanings (e.g., sentiment, controversy, and other state-dependent factors) in explaining the human behaviors from psychology, sociology, cognitive science, and so on, rather than the data per se as in the case of exterior dimensions. It is more intuitive and much easier to understand human behaviors with less redundancy in concept by exploring the behavior interior dimensions, compared with directly using behavior exteriors. However, they usually approach from a unidimensional perspective with a lack of a sense of interrelatedness. Thus, integrating multiple behavior dimensions together into some numerical measures to form a more comprehensive view for subsequent prediction processes becomes a pivotal issue. Moreover, these studies usually focus on the magnitude but neglect the associated temporal features. In this paper, we propose a behavior interior dimension-based neighborhood collaborative filtering method for the top- hashtag adoption frequency prediction that takes into account the interdependence in temporal dynamics. Our proposed approach couples the similarity in user preference and their impact propagation, by integrating the linear threshold model and the enhanced CF model based on behavior interiors. Experiments on Twitter demonstrate that the behavior-interior-aware CF models achieve better adoption prediction results than the state-of-the-art methods, and the joint consideration of similarity in user preference and their impact propagation results in a significant improvement than treating them separately.
URI: https://hdl.handle.net/10356/103679
http://hdl.handle.net/10220/47375
ISSN: 1076-2787
DOI: 10.1155/2018/7371209
Rights: © 2018 Can Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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