Learning and exploiting context dependencies for robust recommendations
Yap, Ghim Eng
Date of Issue2008
School of Computer Engineering
We consider the recommendation problem, where a set of available items or choices are rated and recommended to users accordingly. Over and above the ratings information used in traditional filtering algorithms, the context of the user-recommender interaction is used to improve the recommendation quality. Specifically, we study how the effective learning and exploitation of context dependencies can help to generate more personal and relevant recommendations.
DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications