Authors:
Armelle Brun
and
Anne Boyer
Affiliation:
LORIA-Nancy University, France
Keyword(s):
Recommender systems, m-Commerce, Computation time, Real-time, Usage mining, Association rules.
Related
Ontology
Subjects/Areas/Topics:
Data Engineering
;
Internet Technology
;
Ontologies and the Semantic Web
;
Personalized Web Sites and Services
;
User Modeling
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
;
Web Personalization
;
Web Security and Privacy
;
Wireless Applications
Abstract:
Recommendation technologies have traditionally been used in domains such as e-commerce to recommend resources to customers so as to help them to get the right resources at the right moment. The interest of modelbased collaborative filtering, as sequential association rules, in recommender systems has highly increased over the last few years. These models are usually presented as real-time recommenders. In the last few years, the m-commerce domain has emerged, that displays recommendations on the mobile device instead of the classical screen of the computer. In this paper user privacy preservation is an important objective and one way to be compliant with this constraint is to store the recommender on the mobile-side. Though model-based recommenders are real-time, many of them require a significant time to generate recommendations to users and may not be real-time anymore when implemented on a mobile device. Although some works focused on the way to decrease the time required to compu
te recommendations, the computation complexity still remains relatively high. We put forward a new incremental recommender to get instantaneous recommendations when exploiting usage mining recommender systems in the framework of m-commerce.
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