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Mining Mobile User Preferences for Personalized Context-Aware Recommendation.
- Source :
-
ACM Transactions on Intelligent Systems & Technology . Dec2014, Vol. 5 Issue 4, p1-27. 27p. - Publication Year :
- 2014
-
Abstract
- Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21576904
- Volume :
- 5
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- ACM Transactions on Intelligent Systems & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 100544194
- Full Text :
- https://doi.org/10.1145/2532515