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Web Mining Based OALF Model for Context-Aware Mobile Advertising System
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- Authors
- Yu Chul Jung, Sung Kooc Lim, Jeong Hwan Kim, Sang Ki Kim
- Issue Date
- 2009-06
- Citation
- International Symposium on Integrated Network Management-Workshops (IM) 2009, pp.211-216
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Much of the human activity defines information context and it can be effectively used for a matching process of context-aware mobile advertising systems. Although prior context-aware mobile advertising systems employed personalized and context sensitive approaches, they have mostly used simply aggregated user-side context information from sensors and have relied on previously prepared inference rules when performing match-making. There exist limitations in identifying more diverse variations that can happen in the real world due to the lack of considerations on daily human lives. As a novel solution, in this paper, we address a Natural Language Processing (NLP) combined web mining approach. Especially, we propose an Object-Activity-Location- Feedback (OALF) model which describes what objects are used for a specific activity in a specific location. Most of all, time-variant feedback valences were employed to estimate users' responses to the advertisement triggering entities (objects, activities, locations, and their combinations) in terms of long-term and short-term basis. The model can be realized with a set of web mining procedures including web crawling, data refinement, and sentiment analysis. In addition, we describe how the OALF model can be applied into context-aware mobile advertising and discuss its business model design issues. © 2009 IEEE.
- KSP Keywords
- Business Model Design, Context Information, Context-Aware mobile, Context-sensitive, Data refinement, Design issues, Information Context, Match-making, Matching Process, Natural Language Processing, Real-world