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A System to Support an online Film Club

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96 Pages | chapter 1-5 | PDF and Microsoft Format

Topic Description

A System to Support an online Film Club

Summary/abstract
The intention of this project was to design and develop an online film club with the intent to model and
demonstrate the benefits of recommender systems. The project examined different recommender system technologies and embraced AI theory. Through the analysis of data filtering and information retrieval techniques a recommender system was developed and implemented on the online film club and the data it collected

Contents Page
1. Introduction 1
1.1. Overall Aim Of The Project 1
1.2. Objectives Of The Project 1
1.2.1. The Web Interface 2
1.2.2. The Database 2
1.2.3. The Recommender System 2
1.2.4. Evaluating And Testing 2
1.3. Minimum Requirements 3
1.3.1. Requirement 1 3
1.3.2. Requirement 2 3
1.3.3. Requirement 3 3
1.3.4. Requirement 4 3
1.4. Further Enhancements 4
1.4.1. The Web Interface 4
1.4.2. The Recommender System 4
1.4.3. Administration Pages 4
2. Background Research & Existing Recommender Systems 5
2.1. AI Theory 5
2.1.1. Self-Organised Systems 5
2.1.2. Graceful Degradation 6
2.1.3. Unsupervised Learning 6
2.1.4. Hebbian Learning 6
2.2. Recommender Systems 7
2.2.1. What Is A Recommender System? 7
2.2.2. The Two Paradigms 8
2.2.3. Collaborative Filtering 8
2.2.4. Item-Based Filtering 9
2.2.5. Domain-Portability Of Item-Based Filtering 9
2.2.6. Design Of Collaborative Recommender Systems 10
2.2.7. Design Of Item-Based Recommender Systems 11
2.2.8. Advantages/Disadvantages: Collaborative & Item-Based 12
3. Methodology 12
3.1. Project Methodologies 12
3.2. Project Management 13
3.2.1. Project Schedule 13
3.3. Setting Up The Project 14
3.3.1. The Database Scope & Design 14
IV
3.3.2. Database Management System 15
3.3.3. Database Implementation 15
3.3.4. Database Schema 15
3.3.5. Populating The Database 16
3.3.6. Legal Issues 17
3.3.7. Implementing The Homepage 17
3.3.8. Search Engine Optimization 17
3.3.9. Creating Interest In The Project 18
3.3.10. Contingency Plans 18
3.4. System Requirements 18
3.4.1. Systems Architecture 18
3.4.2. Security Issues 19
3.5. Website Design 20
3.5.1. HCI Paradigms 21
3.5.2. Browsability 22
3.5.3. Cascading Style Sheets (CSS) 22
3.5.4. User Requirements & Accessibility 23
3.5.5. Displaying The Recommendations 23
3.5.6. Hierarchical Task Analysis 23
3.5.7. System Use Case 23
3.5.8. Website Requirements 24
3.6. Recommender System Design 24
3.6.1. Filtering Methodology 24
3.6.2. Building The User Profile 25
3.6.3. Database Views 25
4. Implementation 26
4.1. Implementing The Database 26
4.1.1. Creating And Populating The Database 26
4.1.2. Problems With Mysql 26
4.1.3. Migrating To Postgresql 27
4.2. Implementing The Website 27
4.2.1. Security Issues 27
4.2.2. Web Pages 27
4.2.3. HCI Issues 32
4.3. Implementing The Recommender System 34
4.3.1. Introduction 34
4.3.2. How The Recommender System Works 34
4.3.3. Loss Of Data 35
4.3.4. Enhancement To The Recommender System 35
4.3.5. The Scoring Function 35
V
4.3.6. Is A Member Really A Bestfriend If They Only Rate One Film
The Same As The Session Member? 37
4.3.7. Displaying Member’s Recommendations 37
4.3.8. Future Enhancements 38
4.4. Database Reviewed 39
4.5. Further Enhancements To The System 39
4.5.1. Admin.Php 39
4.5.2. Input.Php 39
5. Testing & Evaluation 41
5.1. Introduction 41
5.2. Functional Testing Of The PHP Scripts 42
5.2.1. Black Box Testing 42
5.2.2. Why Use This Approach To Testing 42
5.3. Usability Testing 43
5.3.1. Focus Groups 43
5.3.2. Results 44
5.4. Recommender System Testing 44
5.4.1. Test 1: Website Feedback 44
5.4.2. Test 2: Focus Groups 46
5.5. Evaluation Of The Recommender System 48
5.5.1. Reflection 48
References 50
Appendix A: Project Reflection
Appendix B: Website Hit Log
Appendix C: Email to Film Society
Appendix D: Web Site Advertisement
Appendix E: Hierarchical Task Analysis
Appendix F: Use Case Diagram
Appendix G: Cascading Style Sheet
Appendix H: Bestfriend Database View

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