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

Date of Award


Document Type

Honors Project

University Scholars Director

Dr. Jeff Keuss

First Reader

Dr. Ryan LaBrie

Second Reader

Prof. Elaine Weltz


Media Recommendation Algorithms, ethics


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.


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

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