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 Naren Ramakrishnan Committee Chair Benjamin J. Keller Committee Member Calvin J. Ribbens Committee Member
Keywords	Collaborative Filtering Random Graphs 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.
Files	Filename       Size       Approximate Download Time (Hours:Minutes:Seconds)     28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access    etd.pdf 3.89 Mb 00:18:01 00:09:16 00:08:06 00:04:03 00:00:20

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