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Graph-Based Analysis for E-Commerce Recommendation GRAPH-BASED ANALYSIS FOR E- COMMERCE RECOMMENDATION Item Type text; Electronic Dissertation Authors Huang, Zan Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 01/10/2021 16:19:34 Link to Item http://hdl.handle.net/10150/196109 GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION by Zan Huang ______________________ Copyright Zan Huang 2005 A Dissertation Submitted to the Faculty of the COMMITTEE ON BUSINESS ADMINISTRATION In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY WITH A MAJOR IN MANAGEMENT In the Graduate College THE UNIVERSITY OF ARIZONA 2005 2 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Zan Huang entitled Graph-Based Analysis for E-Commerce Recommendation and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy _______________________________________________________________________ Date: May 10th, 2005 Hsinchun Chen _______________________________________________________________________ Date: May 10th, 2005 Daniel D. Zeng _______________________________________________________________________ Date: May 10th, 2005 Jay F. Nunamaker, Jr. Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement. _____________________________________________________ Date: May 10th, 2005 Dissertation Co-Director: Hsinchun Chen _____________________________________________________ Date: May 10th, 2005 Dissertation Co-Director: Daniel D. Zeng 3 STATEMENT BY AUTHOR This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at The University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the copyright holder. SIGNED: _________Zan Huang________ 4 ACKNOWLEDGMENTS First of all, I would like to thank my dissertation advisors and mentors, Professors Hsinchun Chen and Daniel D. Zeng. I am indebted to them for their invaluable guidance, inspiration, encouragement, criticism, and suggestions on my dissertation and related research. Without them this dissertation would not have been possible. I also thank my major committee member Professor Jay F. Nunamker for his continued support and encouragement throughout my five years at the University of Arizona. I also thank my minor committee members in the Economics Department, Professors James C. Cox and Alfonso Flores-Lagunes for their guidance and encouragement. I also thank all the faculty members in the Department of MIS for their support. My dissertation has been partly supported by the following grants from National Science Foundation grants: an NSF Digital Library Initiative-II grant, “High-performance Digital Library Systems: From Information Retrieval to Knowledge Management,” IIS-9817473, April 1999 – March 2002, and by an NSF Information Technology Research grant, “Developing a Collaborative Information and Knowledge Management Infrastructure,'' IIS- 0114011, September 2001 – August 2005. It has been an invaluable opportunity for me to work in the Artificial Intelligence Lab under Professor Hinchun Chen's direction. I have benefited enormously from the various research projects and my colleagues in the lab. I worked as Teaching Assistant under the direction of Professor Kurt D. Fenstermacher in my first year of study. I sincerely thank his guidance and encouragement and continued support. I am fortunate to meet an excellent group of doctoral students in the department, who share their insights and ideas. I would like to especially thank Bin Zhu, Chienting Lin, Thian-Huat Ong, Taeha Kim, Michael Chau, Gondy Leroy, Huimin Zhao, Wingyan Chung, Haidong Bi, Jennifer Xu, Dan McDonald, Byron Marshall, Jinwei Cao, Wei Gao, Ming Lin, Jiannan Wang, Gang Wang, Jialun Qin, Yilu Zhou, Yiwen Zhang, Jason Li, Rong Zheng, and Xin Li. 5 DEDICATION To my dearest wife, Fei. 6 TABLE OF CONTENTS LIST OF ILLUSTRATIONS .................................................................................................10 LIST OF TABLES..................................................................................................................11 ABSTRACT............................................................................................................................12 CHAPTER 1: INTRODUCTION..........................................................................................14 1.1 Recommender Systems ..............................................................................................14 1.2 Recommendation Algorithm Research......................................................................17 1.3 Graph-based Analysis for E-commerce Recommendation.......................................18 1.4 Structure of the Dissertation.......................................................................................21 CHAPTER 2: LITERATURE REVIEW...............................................................................28 2.1 Recommendation Approaches and Algorithms.........................................................29 2.1.1 System Input........................................................................................................30 2.1.2 Data Representation ............................................................................................32 2.1.3 Recommendation Approach................................................................................33 2.1.3.1 Content-based and Demographic-based Approaches .................................34 2.1.3.2 Collaborative Filtering .................................................................................35 2.1.3.3 Hybrid Approach..........................................................................................38 2.1.3.4 Knowledge Engineering Approach .............................................................40 2.1.4 Recommendation Research Taxonomy..............................................................41 2.2 Major Problems That Motivate the Research............................................................45 2.2.1 The Sparsity Problem..........................................................................................46 2.2.2 Lack of Unified Integration and Evaluation Framework ...................................50 2.3 Evaluation Methodology and Research Data ............................................................52 2.3.1 Evaluation Methodology.....................................................................................53 2.3.2 Research Data......................................................................................................55 CHAPTER 3: SPREADING ACTIVATION COLLABORATIVE FILTERING ALGORITHMS...............................................................................................................57 3.1 Modeling Recommendation as an Associative Retrieval Problem...........................58 3.1.1 Associative Retrieval and Graph-based Models ................................................58 3.1.2 Collaborative Filtering as Associative Retrieval................................................59 3.1.3 Spreading Activation as Graph Search...............................................................65 3.1.4 Research Questions .............................................................................................67 3.2 Associative Retrieval and Spreading Activation.......................................................68 3.2.1 Constrained Leaky Capacitor Model (LCM) .....................................................69 3.2.2 Branch-and-bound Algorithm (BNB).................................................................70 3.2.3 Hopfield Net Algorithm (Hopfield)....................................................................72 7 TABLE OF CONTENTS - Continued 3.3 An Experimental Study..............................................................................................73 3.3.1 Evaluation Design ...............................................................................................73 3.3.2 Experiment Procedure and Results.....................................................................76 3.3.2.1 The Sparsity Problem...................................................................................76 3.3.2.2 The Cold-start Problem................................................................................78 3.3.2.3 The Over-activation Effect...........................................................................80 3.3.3 Computational Issues with Spreading Activation Algorithms...........................84 3.3.3.1 Sensitivity of Control Parameters................................................................84 3.3.3.2 Computational
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