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Using a Rich Context Model for People-to-People Recommendation
Linnaeus University, Faculty of Technology, Department of Media Technology. (CeLeKT)ORCID iD: 0000-0001-9062-1609
Linnaeus University, Faculty of Technology, Department of Media Technology. (CeLeKT)ORCID iD: 0000-0001-7072-1063
Linnaeus University, Faculty of Technology, Department of Media Technology. (CeLeKT)ORCID iD: 0000-0002-6937-345X
2015 (English)In: 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), 24-26 Aug. 2015, Rome, IEEE, 2015, 703-708 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present an approach for People- to-People recommendations based on a Rich Context Model (RCM). We consider personal user information as contextual information used for our recommendations. The evaluation of our recommendation approach was performed on a social network of students. The obtained results do show a significant increase in performance while, at the same time, a slight increase in quality in comparison to a manual matching process. The proposed approach is flexible enough to handle different data types of contextual information and easy adaptable to other recommendation domains. 

Place, publisher, year, edition, pages
IEEE, 2015. 703-708 p.
Keyword [en]
rich context model, recommendation, matching
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-46067DOI: 10.1109/FiCloud.2015.68ISI: 000378639200105ISBN: 978-1-4673-8103-1 (print)OAI: oai:DiVA.org:lnu-46067DiVA: diva2:851336
Conference
3rd International Conference on Future Internet of Things and Cloud, 24-26 Aug. 2015, Rome
Available from: 2015-09-04 Created: 2015-09-04 Last updated: 2017-04-24Bibliographically approved
In thesis
1. A Rich Context Model: Design and Implementation
Open this publication in new window or tab >>A Rich Context Model: Design and Implementation
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The latest developments of mobile devices include a variety of hardware features that allow for more rich data collection and services. Numerous sensors, Internet connectivity, low energy Bluetooth connectivity to other devices (e.g., smart watches, activity tracker, health data monitoring devices) are just some examples of hardware that helps to provide additional information that can be beneficially used for many application domains. Among others, they could be utilized in mobile learning scenarios (for data collection in science education, field trips), in mobile health scenarios (for health data collection and monitoring the health state of patients, changes in health conditions and/or detection of emergency situations), and in personalized recommender systems. This information captures the current context situation of the user that could help to make mobile applications more personalized and deliver a better user experience. Moreover, the context related information collected by the mobile device and the different applications can be enriched by using additional external information sources (e.g., Web Service APIs), which help to describe the user’s context situation in more details.

The main challenge in context modeling is the lack of generalization at the core of the model, as most of the existing context models depend on particular application domains or scenarios. We tackle this challenge by conceptualizing and designing a rich generic context model. In this thesis, we present the state of the art of recent approaches used for context modeling and introduce a rich context model as an approach for modeling context in a domain-independent way. Additionally, we investigate whether context information can enhance existing mobile applications by making them sensible to the user’s current situation. We demonstrate the reusability and flexibility of the rich context model in a several case studies. The main contributions of this thesis are: (1) an overview of recent, existing research in context modeling for different application domains; (2) a theoretical foundation of the proposed approach for modeling context in a domain-independent way; (3) several case studies in different mobile application domains.

Place, publisher, year, edition, pages
Växjö: Faculty of Technology, Linnaeus University, 2017. 103 p.
Series
Reports: Linnaeus University, Faculty of Technology, 48
Keyword
Context modeling, rich context model, mobile users, current context of the user, mobile sensors, multidimensional vector space model, contextualization
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
urn:nbn:se:lnu:diva-60850 (URN)978-91-88357-62-5 (ISBN)
Presentation
2017-02-17, C1202, Växjö, 09:15 (English)
Opponent
Supervisors
Available from: 2017-02-24 Created: 2017-02-22 Last updated: 2017-09-01Bibliographically approved

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Sotsenko, AlisaJansen, MarcMilrad, Marcelo
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
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Output format
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