Title page for ETD etd-02282001-175040


Type of Document Master's Thesis
Author Mirza, Batul J
URN etd-02282001-175040
Title Jumping Connections: A Graph-Theoretic Model for Recommender Systems
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Ramakrishnan, Naren Committee Chair
Keller, Benjamin J. Committee Member
Ribbens, Calvin J. Committee Member
Keywords
  • Random Graphs
  • Collaborative Filtering
  • Recommender Systems
Date of Defense 2001-02-08
Availability unrestricted
Abstract
Recommender systems have become paramount to customize

information access and reduce information overload. They serve multiple

uses, ranging from suggesting products and artifacts (to consumers), to

bringing people together by the connections induced by (similar) reactions

to products and services. This thesis presents a graph-theoretic model that

casts recommendation as a process of 'jumping connections' in a graph. In

addition to emphasizing the social network aspect, this viewpoint provides a

novel evaluation criterion for recommender systems. Algorithms for

recommender systems are distinguished not in terms of predicted ratings of

services/artifacts, but in terms of the combinations of people and artifacts

that they bring together. We present an algorithmic framework drawn from

random graph theory and outline an analysis for one particular form of jump

called a 'hammock.' Experimental results on two datasets collected over the

Internet demonstrate the validity of this approach.

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