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Enhance Nmf-Based Recommendation Systems with Auxiliary Information Imputation University of Kentucky UKnowledge Theses and Dissertations--Computer Science Computer Science 2019 ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION Fatemah Alghamedy University of Kentucky, [email protected] Digital Object Identifier: https://doi.org/10.13023/etd.2019.144 Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Alghamedy, Fatemah, "ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION" (2019). Theses and Dissertations--Computer Science. 79. https://uknowledge.uky.edu/cs_etds/79 This Doctoral Dissertation is brought to you for free and open access by the Computer Science at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Computer Science by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known. I agree that the document mentioned above may be made available immediately for worldwide access unless an embargo applies. I retain all other ownership rights to the copyright of my work. I also retain the right to use in future works (such as articles or books) all or part of my work. I understand that I am free to register the copyright to my work. REVIEW, APPROVAL AND ACCEPTANCE The document mentioned above has been reviewed and accepted by the student’s advisor, on behalf of the advisory committee, and by the Director of Graduate Studies (DGS), on behalf of the program; we verify that this is the final, approved version of the student’s thesis including all changes required by the advisory committee. The undersigned agree to abide by the statements above. Fatemah Alghamedy, Student Dr. Jun Zhang, Major Professor Dr. Miroslaw Truszczynski, Director of Graduate Studies ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION |||||||||||||| DISSERTATION |||||||||||||| A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the College of Engineering at the University of Kentucky By Fatemah Alghamedy Lexington, Kentucky Director: Jun Zhang, Ph.D., Professor of Computer Science Lexington, Kentucky 2019 Copyright c Fatemah Alghamedy 2019 ABSTRACT OF DISSERTATION ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in the recommendation system is the rating that expresses the user's opinion about an item. One of the most significant issues in the recommendation systems is the lack of ratings. This issue is called "cold- start" issue, which appears clearly with New-Users who did not rate any item and New-Items, which did not receive any rating. The traditional recommendation systems assume that users are independent and identically distributed and ignore the connections among users whereas the recommendation actually is a social activity. This dissertation aims to enhance NMF-based recommendation systems by utilizing the imputation method and lim- iting the errors that are introduced in the system. External information such as trust network and item categories are incorporated into NMF-based recommen- dation systems through the imputation. The proposed approaches impute various subsets of the missing ratings. The subsets are defined based on the total number of the ratings of the user or item before the imputation, such as impute the missing ratings of New-Users, New- Items, or cold-start users or items that suffer from the lack of the ratings. In addition, several factors are analyzed that affect the prediction accuracy when the imputation method is utilized with NMF-based recommendation systems. These factors include the total number of the ratings of the user or item before the im- putation, the total number of imputed ratings for each user and item, the average of imputed rating values, and the value of imputed rating values. In addition, several strategies are applied to select the subset of missing ratings for the impu- tation that lead to increasing the prediction accuracy and limiting the imputation error. Moreover, a comparison is conducted with some popular methods that are in common with the proposed method in utilizing the imputation to handle the lack of ratings, but they differ in the source of the imputed ratings. Experiments on different large-size datasets are conducted to examine the pro- posed approaches and analyze the effects of the imputation on accuracy. Users and items are divided into three groups based on the total number of the ratings before the imputation is applied and their recommendation accuracy is calculated. The results show that the imputation enhances the recommendation system by capacitating the system to recommend items to New-Users, introduce New-Items to users, and increase the accuracy of the cold-start users and items. However, the analyzed factors play important roles in the recommendation accuracy and limit the error that is introduced from the imputation. KEYWORDS: recommendation system, collaborative filtering, NMF, trust ma- trix, imputation. Fatemah Alghamedy ||||||||||||{ April 16, 2019 ||||||||||||{ ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION By Fatemah Alghamedy Jun Zhang, Ph.D. ||||||||||||{ Director of Dissertation Miroslaw Truszczynski, Ph.D. ||||||||||||{ Director of Graduate Studies April 16, 2019 ||||||||||||{ Date To my father, my mother, my husband, my daughter, my son, my sisters, and my brother. ACKNOWLEDGMENTS I would like to express my gratitude to the people who helped and encouraged me during my Ph.D. journey. First of all, I would like to express the deepest appreciation to my academic advisor, Prof. Jun Zhang, for his continuous support, patience, motivation, en- thusiasm, and immense knowledge. His guidance helped me throughout my time of researching and writing this dissertation. Next, I would like to thank the committee faculty members Dr. Jerzy W. Jaromczyk (Department of Computer Science), Dr. Jinze Liu (Department of Computer Science), Dr. Sen-ching Samson Cheung (Department of Electrical and Computer Engineering), and the outside examiner Prof. Qiang Ye (Department of Mathematics) for their time and the helpful comments on my dissertation. Also, I would like to express my special thanks to the Computer Science De- partment and to UK Research Computing (UKRC) for their research resources. In addition, I would like to thank the Imam Abdulrahman bin Faisal University and Saudi Arabian Cultural Mission (SACM) for their full financial support for the entirety of my Ph.D. years. Last but not least, I would like to thank my family for their unlimited support, especially my father and my mother, whom I wouldn't be able to reach this success without their care and support ever since I was a child. My father taught me the meaning of determination and ambition and was always there when I needed any advice or opinion, especially concerning the academic field. My mother surrounded me by her love and care and never forgot to support me and take care of me even though there were thousands of miles distance between us. A special thanks to my adorable children, Aseel and Suhail. Thank you for bearing with me on this journey, as I'm mostly busy; and above all of that, thank you for being a source of strength for me. Having you around me gave me the will to continue and to keep moving forward. iii TABLE OF CONTENTS Acknowledgments iii List of Tables vii List of Figures ix 1 Introduction 1 1.1 Related Works . .5 1.2 Dissertation Organization . 10 1.2.1 Technical Contributions . 10 1.2.2 Notational Conventions . 11 1.2.3 Data Description . 11 1.2.4 Evaluation Strategy . 14 2 Imputation with Item Auxiliary Information 16 2.1 Problem Description . 16 2.2 Proposed Method . 19 2.2.1 Objective Function . 21 2.2.2 Update Formula . 21 2.2.3 Convergence Analysis . 22 2.2.4 Detailed Algorithm . 23 2.2.5 Complexity . 25 2.3 Experimental Study . 28 2.3.1 The Influence of Imputed Rating Value . 30 2.3.2 Parameter Study . 34 2.4 Summary . 36 3 Imputation with Trust Network Information for New Users 38 3.1 Problem Description . 38 3.2 Proposed Method . 41 3.2.1 Objective Function . 41 3.2.2 Update Formula . 42 3.2.3 Convergence Analysis . 42 3.2.4 Detailed Algorithm . 42 3.2.5 Complexity . 44 3.3 Experimental Study . 45 3.3.1 Imputation Vs. WNMTF . 48 3.4 Summary . 50 4 Influential Factors of Imputation with Trust Network Information for Cold-Start Users 51 4.1 Problem Description . 51 4.2 Proposed Method . 52 4.2.1 Objective Function . 54 4.2.2 Update Formula . 55 iv 4.2.3 Convergence Analysis . 55 4.2.4 Detailed Algorithm . 55 4.2.5 Complexity . 57 4.3 Experimental Study . 57 4.3.1 Influence of the Rating Value Average . 62 4.3.2 Parameter Settings . 63 4.3.3 Summary of Results . 66 4.4 Summary . 67 5 Selective Imputation Strategies Based on Fused Factored Matri- ces 68 5.1 Problem Description . 68 5.2 Proposed Method .
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