Multi criteria Pseudo rating and Multidimensional User profile for Movie Recommender System
Recommender systems are usually classified into three categories based on how recommendations are made (i) Content–Based recommendations, (ii) Collaborative Filtering recommendations and (iii) Hybrid recommendations. To reduce the Sparsity Rating problem and fulfill the co-rated items in CF table, the current systems create the pseudo ratings usually based on one criteria. This paper proposes pseudo ratings based on multi criteria and also concentrates on the contextual information as multidimensional. To do the pseudo ratings based on multi criteria, the Na?ve Bayes is applied to classify the multi criteria of user’s preferences. To incorporate multidimensional, the multi regression is applied to analyze the contextual information of user. According to the experimental evaluation, the recommender system on movie domain called ModernizeMovie is created and shows that the multi criteria pseudo ratings and multidimensional user profile enhances the quality and accuracy of recommendation results.
recommender system collaborative filtering multi criteria multidimensional pseudo rating
Nutcha Rattanajitbanjong Saranya Maneeroj
Department of Mathematics, Faculty of Science Chulalongkorn University, CU Bangkok, Thailand
国际会议
北京
英文
1257-1262
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）