Thsis (M.Sc.)--Chulalongkorn University, 2008

Most of current Recommender Systems based on Content-Based Filtering, Collaborative Filtering, Demographic Filtering and Hybrid Filtering which are concentrated on user and item entities. Many research papers are improved by pointing out either Multiple Criteria Rating approach or Multidimensional approach for Recommender System. This paper proposes an advanced Recommender System to provide higher quality of recommendations by combining the Multiple Criteria rating and the Multidimensional approaches. For the Multiple Criteria approach, this paper proposed a method that changes the way of weighting to be more suitable and also concern about the frequency of the selection movie features. To do Multidimensional approach, the Multiple Linear Regression is applied to analyze the contextual information of user characteristics. According to the experimental evaluation, the combining of Multiple Criteria Rating and Multidimensional approaches provide more accurate recommendation results than the current Hybrid Recommender Systems.