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http://hdl.handle.net/10397/95961
Title: | A learning based human interaction modeling using mobile sensing | Authors: | Kulshrestha, T Saxena, D Niyogi, R |
Issue Date: | 2019 | Source: | 17th IEEE International Conference on Parallel and Distributed Processing with Applications, 9th IEEE International Conference on Big Data and Cloud Computing, 9th IEEE International Conference on Sustainable Computing and Communications, 12th IEEE International Conference on Social Computing and Networking, ISPA/BDCloud/SustainCom/SocialCom 2019, Xiamen, China, 16-18 December 2019, p. 1049-1058 | Abstract: | Online social networks are emerging as a convenient platform where users build social relations with other individuals having similar interests, family/work background, etc. However, existing human interaction modeling is based on social graphs which are not more precise for friend suggestions in real-life. In this paper, we leverage the basic feats of deep learning for developing human interaction system, named MyCompanion, based on the user's lifestyle/activity information collected using the mobile crowd sensing. We collect a user's local knowledge, such as local information, ambient, and activity type, activity location and activity time. Then, the collected information is further aggregated and transferred to the deep learning enabled cloud server for user's daily schedule/activities analysis. We propose a schedule matching algorithm which finds the similarity among individuals' activities w.r.t. their activity type, activity time and activity location to recommend the most suitable friend(s) to the users. We develop a real-time testbed to perform a spatio-temporal analysis of the collected data from the users' smartphones. We also perform several experiments for evaluating the system performance. Our proof-of-concept prototype shows the usability of the proposed system. | Keywords: | Crowdsourcing Friend's suggestion Human interactions Lstm Mobile crowd sensing Recurrent neural networks Similarity index Social networks |
Publisher: | IEEE | ISBN: | 978-1-7281-4328-6 (Electronic) 978-1-7281-4329-3 (Print on Demand(PoD)) |
DOI: | 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00150 | Rights: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication T. Kulshrestha, D. Saxena and R. Niyogi, "A Learning Based Human Interaction Modeling Using Mobile Sensing," 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 2019, pp. 1049-1058 is available at https://dx.doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00150. |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Kulshrest_Learning_Mobile_Sensing.pdf | Pre-Published version | 1.81 MB | Adobe PDF | View/Open |
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