Collaborative Filtering

ISBN-13: 9783659429095 / Angielski / Miękka / 2013 / 164 str.

Su Xiaoyuan

Opis

Collaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendations from highly sparse user rating data. My contributions to this research topic include proposing the frameworks of imputation-boosted collaborative filtering (IBCF) and imputed neighborhood based collaborative filtering (INCF). We also proposed a model-based CF technique, TAN-ELR CF, and two hybrid CF algorithms, sequential mixture CF and joint mixture CF. Empirical results show that our proposed CF algorithms have very good predictive performances. In the investigation of applying imputation techniques in mining incomplete data, we proposed imputation-helped classifiers, and VCI predictors (voting on classifications from imputed learning sets), both of which resulted in significant improvement in classification performance for incomplete data over conventional machine learned classifiers. The main imputation techniques involved in these algorithms include EM (expectation maximization) and BMI (Bayesian multiple imputation).

O autorze

Dr. Xiaoyuan Su received his PhD degree in Computer Science from Florida Atlantic University in 2008, Master degree in ECE from University of Alberta in 2004, and MBA degree from University of Washington in 2012. He has published about 20 refereed papers in data mining, machine learning, and artificial intelligence. He is a senior member of IEEE.