Title

Reading the Mind of the Internet: A Study of Collaborative Recommendation Algorithms

Date of Award

2014

Document Type

Honors Project

University Scholars Director

Dr. Jeff Keuss

First Advisor/Committee Member

Dr. Ryan LaBrie

Second Advisor/Committee Member

Prof. Elaine Weltz

Keywords

Media Recommendation Algorithms, ethics

Abstract

Recommender systems are emerging as a key way to manage data on the Internet. In this paper, an overview of different recommender systems is presented, including collaborative, content-based, knowledge-based, and hybrid algorithms. Each of these methods is examined for strengths, weaknesses, and preferred content. Based on this research, the design and implementation of a real-world recommender is explained as a proof of concept. The Webcomic Companion, an online webcomic recommendation system, is outlined, including design, implementation, and testing results. The main components of the system are a PHP website, a MySQL database, and a recommendation algorithm. The key algorithm in the website is an item-based collaborative system augmented with content-based features. On a small test dataset, the algorithm was found to have an average 33% success rate. Also discussed are the ethical consequences of recommendation systems, including privacy and data diversity.

Comments

A project submitted in partial fulfillment of the requirements of the University Scholars Program.

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