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Internet Search Assistant Based on the Random Neural Network

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Internet Search Assistant Based on the Random Neural Network Internet Search Assistant based on the Random Neural Network Thesis submitted for the Degree of Doctor of Philosophy of the University of London and the Diploma of Imperial College June 2018 Supervisor: Professor Erol Gelenbe Guillermo Serrano Bermejo (Will Serrano) [email protected] Intelligent Systems and Networks Group Electrical and Electronic Engineering Department Imperial College London This work is on my own and else is appropriately referenced. ‘The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any use reuse or redistribution, researches must make clear to others the licence terms of this work’ Page 2 of 247 I would like to thank and express my admiration and gratitude to Professor Erol Gelenbe; his personal and academic support during my challenging research studies has made this PhD and Thesis real. I feel privileged to have been to Professor Erol Gelenbe’ PhD student; sharing some of his ideas has been very rewarding. Professor Erol Gelenbe has made an exceptional academic and industrial contribution to the Artificial Intelligence and Machine Learning research field. In addition; I would like to express my gratitude to my viva examiners, search validators and Imperial College London. This Thesis and research work is dedicated to its readers. Acknowledgments Page Page 3 of 247 Blank Page Page 4 of 247 Table of Contents Abstract ............................................................................................................12 1 Introduction ............................................................................................13 1.1 Research Proposal ................................................................................... 14 1.2 Related Work .......................................................................................... 15 1.3 Summary of Contributions ........................................................................ 21 1.4 Summary of Publications .......................................................................... 23 2 Web Search .............................................................................................24 2.1 Internet Assistants .................................................................................. 24 2.2 Web Search Engines ................................................................................ 26 2.3 Metasearch Engines ................................................................................. 27 2.4 Web result clustering ............................................................................... 30 2.5 Travel Services ....................................................................................... 33 2.6 Citation Analysis ..................................................................................... 36 3 Ranking ...................................................................................................38 3.1 Ranking Algorithm ................................................................................... 38 3.2 Relevance Metrics ................................................................................... 43 3.3 Learning to Rank ..................................................................................... 48 4 Recommender Systems ...........................................................................50 4.1 Recommender System Types .................................................................... 50 4.2 Recommender System Relevance Metrics ................................................... 51 4.3 Recommender System Model .................................................................... 53 5 The Random Neural Network ...................................................................54 5.1 Neural Networks ..................................................................................... 54 5.2 Deep Learning ........................................................................................ 55 5.3 G-Networks ............................................................................................ 56 5.4 The Random Neural Network .................................................................... 58 5.5 The Deep Learning Cluster Random Neural Network .................................... 67 5.6 Random Neural Network Extensions .......................................................... 77 5.7 Random Neural Network Applications ......................................................... 79 6 Internet Search Assistant Model .............................................................87 Page 5 of 247 6.1 Intelligent Search Assistant Model ............................................................. 87 6.2 Result Cost Function ................................................................................ 88 6.3 User iteration .......................................................................................... 91 6.4 Dimension Learning ................................................................................. 94 6.5 Gradient Descent Learning ....................................................................... 96 6.6 Reinforcement Learning ........................................................................... 97 7 Unsupervised Evaluation .........................................................................99 7.1 Implementation ...................................................................................... 99 7.2 Spearman's Rank Correlation Coefficient .................................................. 101 7.3 Google Search ...................................................................................... 101 7.4 Web Search Evaluation .......................................................................... 104 7.5 Metasearch Evaluation ........................................................................... 107 8 User Evaluation – First Iteration ...........................................................111 8.1 Implementation .................................................................................... 111 8.2 Quality Metric ....................................................................................... 113 8.1 Google Search – Result Cost Function ...................................................... 113 8.2 Web Search – Result Cost Function.......................................................... 115 8.3 Google Search - Fixed Query – Relevant Centre Point ................................ 116 8.4 Google Search – Open Query - Relevant Centre Point ................................ 117 9 User Evaluation – Learning algorithms ..................................................119 9.1 Quality Metric ....................................................................................... 119 9.2 Web Search Evaluation .......................................................................... 120 9.3 Academic Database Evaluation ................................................................ 128 9.4 Recommender System Evaluation............................................................ 141 10 User Evaluation – Deep Learning ...........................................................158 10.1 Implementation .................................................................................... 159 10.2 Evaluation ............................................................................................ 159 10.3 Experimental Results ............................................................................. 161 11 Conclusions ...........................................................................................168 12 References ............................................................................................171 Appendix ........................................................................................................190 A ISA Screen shots ............................................................................................ 190 Page 6 of 247 B Unsupervised Evaluation .................................................................................. 191 C User Evaluation – First Iteration ....................................................................... 192 Google Search – Result Cost Function .................................................................. 192 Web Search – Result Cost Function ...................................................................... 194 D User Evaluation – Learning Algorithms .............................................................. 202 E User Evaluation – Deep Learning ...................................................................... 239 Page 7 of 247 List of Figures Figure 1: Web search engine architecture ................................................................26 Figure 2: Metasearch services model ......................................................................28 Figure 3: Metasearch engine architecture ................................................................29 Figure 4: Web Cluster Engine Architecture ...............................................................31 Figure 5: Traditional travel services model ...............................................................34 Figure 6: Online travel services model .....................................................................35 Figure
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