Jumping Connections: A Graph-Theoretic Model for Recommender Systems

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2001

Author dc:creator

• Mirza, Batul J.

Chair dc:contributor.committeechair

• Ramakrishnan, Naren

Committee members dc:contributor.committeemember

• Keller, Benjamin J.
• Ribbens, Calvin J.

• Random Graphs
• Collaborative Filtering
• Recommender Systems

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-02282001-175040

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/31370