Adaptive content recommendation for mobile users: Ordering recommendations using a hierarchical context model with granularity

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Retrieving timely and relevant information on-site is an important task for mobile users. A context-aware system can understand a user's information needs and thus select contents according to relevance. We propose a context-dependent search engine that represents user context in a knowledge-based context model, implemented in a hierarchical structure with granularity information. Search results are ordered based on semantic relevance computed as similarity between the current context and tags of search results. Compared against baseline algorithms, the proposed approach enhances precision by 22% and pooled recall by 17%. The use of size-based granularity to compute similarity makes the approach more robust against changes in the context model in comparison to graph-based methods, facilitating import of existing knowledge repositories and end-user defined vocabularies (folksonomies). The reasoning engine being light-weight, privacy protection is ensured, as all user information is processed locally on the user's phone without requiring communication with an external server.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2014-08
Language
English
Article Type
Article
Keywords

AWARE APPLICATIONS; INFRASTRUCTURE; FRAMEWORK; GUIDE

Citation

PERVASIVE AND MOBILE COMPUTING, v.13, pp.85 - 98

ISSN
1574-1192
DOI
10.1016/j.pmcj.2013.11.002
URI
http://hdl.handle.net/10203/190506
Appears in Collection
GCT-Journal Papers(저널논문)
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