Behavior-interior-aware user preference analysis based on social networks
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...
| Auteurs principaux: | , , , , , , |
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| Autres auteurs: | |
| Format: | Journal Article |
| Langue: | English |
| Publié: |
2019
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| Sujets: | |
| Accès en ligne: | https://hdl.handle.net/10356/103679 http://hdl.handle.net/10220/47375 |
| _version_ | 1826128948147257344 |
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| author | Wang, Can Bo, Tao Zhao, Yun Wei Chi, Chi-Hung Lam, Kwok-Yan Wang, Sen Shu, Min |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Wang, Can Bo, Tao Zhao, Yun Wei Chi, Chi-Hung Lam, Kwok-Yan Wang, Sen Shu, Min |
| author_sort | Wang, Can |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:33:11Z |
| format | Journal Article |
| id | ntu-10356/103679 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:33:11Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/1036792020-03-07T11:50:49Z Behavior-interior-aware user preference analysis based on social networks Wang, Can Bo, Tao Zhao, Yun Wei Chi, Chi-Hung Lam, Kwok-Yan Wang, Sen Shu, Min School of Computer Science and Engineering Social Networks DRNTU::Engineering::Computer science and engineering Behavior Interiors 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. Published version 2019-01-04T07:12:27Z 2019-12-06T21:17:44Z 2019-01-04T07:12:27Z 2019-12-06T21:17:44Z 2018 Journal Article 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 1076-2787 https://hdl.handle.net/10356/103679 http://hdl.handle.net/10220/47375 10.1155/2018/7371209 en Complexity © 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. 18 p. application/pdf |
| spellingShingle | Social Networks DRNTU::Engineering::Computer science and engineering Behavior Interiors Wang, Can Bo, Tao Zhao, Yun Wei Chi, Chi-Hung Lam, Kwok-Yan Wang, Sen Shu, Min Behavior-interior-aware user preference analysis based on social networks |
| title | Behavior-interior-aware user preference analysis based on social networks |
| title_full | Behavior-interior-aware user preference analysis based on social networks |
| title_fullStr | Behavior-interior-aware user preference analysis based on social networks |
| title_full_unstemmed | Behavior-interior-aware user preference analysis based on social networks |
| title_short | Behavior-interior-aware user preference analysis based on social networks |
| title_sort | behavior interior aware user preference analysis based on social networks |
| topic | Social Networks DRNTU::Engineering::Computer science and engineering Behavior Interiors |
| url | https://hdl.handle.net/10356/103679 http://hdl.handle.net/10220/47375 |
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