Please use this identifier to cite or link to this item:
https://doi.org/10.1145/3086676
DC Field | Value | |
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dc.title | Tweet can be Fit: Integrating Data from Wearable Sensors and Multiple Social Networks for Wellness Profile Learning | |
dc.contributor.author | Aleksandr Farseev | |
dc.contributor.author | Tat-Seng Chua | |
dc.date.accessioned | 2020-05-21T06:57:20Z | |
dc.date.available | 2020-05-21T06:57:20Z | |
dc.date.issued | 2017-08-24 | |
dc.identifier.citation | Aleksandr Farseev, Tat-Seng Chua (2017-08-24). Tweet can be Fit: Integrating Data from Wearable Sensors and Multiple Social Networks for Wellness Profile Learning. ACM Transactions on Information Systems 35 (4). ScholarBank@NUS Repository. https://doi.org/10.1145/3086676 | |
dc.identifier.issn | 10468188 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/168372 | |
dc.description.abstract | Wellness is a widely popular concept that is commonly applied to fitness and self-help products or services. Inference of personal wellness-related attributes, such as body mass index (BMI) category or disease tendency as well as understanding of global dependencies between wellness attributes and users' behavior, is of crucial importance to various applications in personal and public wellness domains. At the same time, the emergence of social media platforms and wearable sensors makes it feasible to perform wellness profiling for users from multiple perspectives. However, research efforts on wellness profiling and integration of social media and sensor data are relatively sparse. This study represents one of the first attempts in this direction. Specifically, we infer personal wellness attributes by utilizing our proposed multisource multitask wellness profile learning framework-WellMTL-which can handle data incompleteness and perform wellness attributes inference from sensor and social media data simultaneously. To gain insights into the data at a global level, we also examine correlations between first-order data representations and personal wellness attributes. Our experimental results show that the integration of sensor data and multiple social media sources can substantially boost the performance of individual wellness profiling. 2017 Copyright is held by the owner/author. | |
dc.publisher | Association for Computing Machinery | |
dc.subject | Multiple Sources Integration | |
dc.subject | Wellness Profile Learning | |
dc.subject | Multi-Task Learning | |
dc.subject | Wearable Sensors | |
dc.subject | Personal Lifestyle Assistance | |
dc.type | Article | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.doi | 10.1145/3086676 | |
dc.description.sourcetitle | ACM Transactions on Information Systems | |
dc.description.volume | 35 | |
dc.description.issue | 4 | |
dc.grant.id | R-252-300-002-490 | |
dc.grant.fundingagency | Infocomm Media Development Authority | |
dc.grant.fundingagency | National Research Foundation | |
Appears in Collections: | Staff Publications Elements |
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File | Description | Size | Format | Access Settings | Version | |
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Tweet can be Fit - Integrating Data from Wearable Sensors and Multiple Social Networks for Wellness Profile Learning.pdf | 2.62 MB | Adobe PDF | OPEN | None | View/Download |
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