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Collabortive filtering using machine learning and statistical techniques

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Date Issued:
2008
Summary:
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, including kNN, neural network, one rule, decision table, SVM, logistic regression, decision tree (C4.5), random forest, and decision list (PART), and the well known Bagging predictors. The main imputation techniques involved in these algorithms include EM (expectation maximization) and BMI (Bayesian multiple imputation).
Title: Collabortive filtering using machine learning and statistical techniques.
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Name(s): Su, Xiaoyuan.
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 2008
Publisher: Florida Atlantic University
Physical Form: electronic
Extent: xv, 139 p. : ill. (some col.).
Language(s): English
Summary: 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, including kNN, neural network, one rule, decision table, SVM, logistic regression, decision tree (C4.5), random forest, and decision list (PART), and the well known Bagging predictors. The main imputation techniques involved in these algorithms include EM (expectation maximization) and BMI (Bayesian multiple imputation).
Identifier: 317858043 (oclc), 186301 (digitool), FADT186301 (IID), fau:2868 (fedora)
Note(s): by Xiaoyuan Su.
Vita.
Thesis (Ph.D.)--Florida Atlantic University, 2008.
Includes bibliography.
Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.
Subject(s): Filters (Mathematics)
Machine learning
Data mining -- Technological innovations
Database management
Combinatorial group theory
Held by: FBoU FAUER
Persistent Link to This Record: http://purl.flvc.org/FAU/186301
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU