Recommender systems are efficient tools that overcome the information overload problem by providing users with the most relevant contents. This is generally done through user's preferences/ratings acquired from log files of his former sessions. Besides these preferences, taking into account the interaction context of the user will improve the relevancy of recommendation process. In this paper, we propose a context-aware recommender system based on both user profile and context. The approach we present is based on a previous work on data personalization which leads to the definition of a Personalized Access Model that provides a set of personalization services. We show how these services can be deployed in order to provide advanced context-aware recommender systems.
|Publication status||Published - 1 Dec 2009|
|Event||3rd International Workshop on "Personalized Access, Profile Management, and Context Awareness in Databases", PersDB 2009 - In Conjunction with VLDB 2009 - Lyon, France|
Duration: 28 Aug 2009 → 28 Aug 2009
|Other||3rd International Workshop on "Personalized Access, Profile Management, and Context Awareness in Databases", PersDB 2009 - In Conjunction with VLDB 2009|
|Period||28/8/09 → 28/8/09|
ASJC Scopus subject areas
- Information Systems