Title:
A Target-Oriented Decision Approach for Personalized Ranking in Recommendation Services

Speaker:
Van-Nam Huynh
School of Knowledge Science, Japan Advanced Institute of Science and Technology

Abstract:
Personalized recommendation is very useful for users in their decision-making process when they want to select some item(s) from a large number of candidates using their personal preferences, particularly in e-commerce applications. In this talk, we will introduce a target-based decision approach to multiple-criteria ranking for personalized recommendations. The central idea of this approach is to interpret a particular user's request as a target (or benchmark) at which the user would be only interested in candidates meeting this target. Then, the proposed procedure for personalized ranking consists of defining a target-oriented multiple-criteria evaluation function, according to user's request, that quantifies how well a candidate meets the user's target. Finally, we conclude the talk by highlighting some directions for future research.