GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
WordNet: a lexical database for English
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
Knowledge and Information Systems
A semantic-expansion approach to personalized knowledge recommendation
Decision Support Systems
Journal of Systems and Software
Receiver-side semantic reasoning for digital TV personalization in the absence of return channels
Multimedia Tools and Applications
Hybrid web recommender systems
The adaptive web
A novel personalized paper search system
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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In this paper, we propose a personalized recommendation system for mobile application software (app) to mobile user using semantic relations of apps consumed by users. To do that, we define semantic relations between apps consumed by a specific member and his/her social members using Ontology. Based on the relations, we identify the most similar social members from the reasoning process. The reasoning is explored from measuring the common attributes between apps consumed by the target member and his/her social members. The more attributes shared by them, the more similar is their preference for consuming apps. We also develop a prototype of our system using OWL (Ontology Web Language) by defining ontology-based semantic relations among 50 mobile apps. Using the prototype, we showed the feasibility of our algorithm that our recommendation algorithm can be practical in the real field and useful to analyze the preference of mobile user.