Developing a Restaurant Recommender System

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  • Topic: Cold start, Recommender system, Implicit data collection
  • Pages : 45 (12204 words )
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  • Published : March 28, 2012
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Developing a Restaurant Recommender System
( http://affirmation.mine.nu )

Written by Fredrik Kalseth
Supervised by Marilyn Walker

4th of May, 2005
Module: COM3010 / COM3021. This report is submitted in partial fulfillment of the requirement for the degree of Bachelor of Computer Science by Fredrik Kalseth.

I

Declaration
All sentences or passages quoted in this report from other people's work have been specifically acknowledged by clear cross-referencing to author, work and page(s). Any illustrations which are not the work of the author of this report have been used with the explicit permission of the originator and are specifically acknowledged. I understand that failure to do this amounts to plagiarism and will be considered grounds for failure in this project and the degree examination as a whole.

Name: Signature: Date:

Fredrik Kalseth _____________ 4th of May, 2005

II

Abstract
Recommender systems have become an important asset of the information age, and this dissertation is about the application of such a system within the domain of restaurants. The first part of the dissertation aims to explain and discuss some of the important research that has been done on recommender systems since they were first introduced in the early 90’s. The analysis will focus on the general need and application of such systems, before taking a closer look at collaborative filtering and knowledge-based recommender systems, how they work, what their shortcomings are and what remedies have been proposed. The second part of this dissertation is the implementation of a restaurant recommender website which, apart from successfully implementing a recommender system, aims to evaluate the utility and usability of such a system within the restaurant domain.

III

Acknowledgements
Thanks to: My supervisor Marilyn Walker, for great insight and inspiration. Everyone who helped with testing, and everyone who took the survey, The .NET communities, (www.asp.net, www.codeproject.com, www.theserverside.net …) for publishing excellent free resources and tutorials on everything .NET. Google, for always being there for me in times of need.

IV

Contents
DECLARATION.....................................................................................................................................I ABSTRACT........................................................................................................................................... II ACKNOWLEDGEMENTS.................................................................................................................. III CONTENTS ......................................................................................................................................... IV TABLE OF FIGURES ......................................................................................................................... VI 1 2 INTRODUCTION.......................................................................................................................... 1 LITERATURE REVIEW .............................................................................................................. 3 2.1 2.2 2.3 2.4 2.5 2.5.1 2.5.2 2.5.3 2.5.4 2.6 2.7 3 INTRODUCTION TO RECOMMENDER SYSTEMS ............................................................................ 3 RECOMMENDER SYSTEMS, HOW DO THEY WORK? .................................................................... 6 COLLABORATIVE FILTERING ..................................................................................................... 7 KNOWLEDGE-BASED FILTERING .............................................................................................. 11 BRIDGING THE GAP – WHERE RECOMMENDER SYSTEMS FAIL .................................................. 14 Data Sparseness ................................................................................................................ 14 Cold...
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