ABSTRACT
This chapter presents a time-aware recommendation strategy that solves the problems by linking an ontology of commercial products to parameterized time functions, whose values are adjusted per the users’ membership in consumption stereotypes. It explores the possibilities of the Semantic Web technologies for generating automatically interactive e-commerce services that provide the users with personalized commercial functionalities related to the selected items. The chapter discusses the main parts of our personalization cloud, including the ontology, user profiles, and consumption stereotypes, along with the time dependence curves adopted in our time-aware approach. The research community has proposed two main filtering paradigms for recommender systems, namely content-based and collaborative filtering, which are commonly combined in hybrid approaches. Collaborative filtering recommends to a user items which have been appealing to others with similar preferences. In view of the limitations of content-based and collaborative systems, researchers have commonly opted for hybrid approaches to exploit the advantages of both filtering strategies, and to mitigate their deficiencies.
