MINING INFLUENCE IN RECOMMENDER SYSTEMS A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA Al Mamunur rashid IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY John T. Riedl. Adviser Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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UMI Number 3250160 Copyright 2007 by Rashid. Al Mamunur All rights reserved NFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignment can adversely affect reproduction In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion UMI UMI Microform 3250160 Copyright 2007 by ProQuest Information and Learning Company All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code ProQuest Information and Learning Company 300 North Zeeb road P O. Box 1346 Ann Arbor. MI 48106-1346 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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C Al Mamunur Rashid 2007 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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To Tulip Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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Abstract The influence of an entity can be defined as its ability to affect the conduct, behav or actions of other entities. We provide evidence in this thesis that influence is important in recommender systems. Recommender systems help people find the things they care about from an unmanageably large number of choices by mining relationships between like-ininded people. Discovering influential items and users can enhance the ability of a recommender system to deliver quality recommendations in various ways, including guiding a new member through the right set of items to evaluate so that the system learns her preferences effectively, and selecting reliable users for early evaluations of new items. How, then, may we discover the most influential items and users in the system? We explore several sources of insight for influence algorithms in recommender systems: social network theory, information theory and mathematical analysis of the recommender algorithms themselves. Broadly speaking a)the nature of prior research on influence in other domains and the viability of applying that research to the recommender systems domain, c)new measures of infuence, based on prior research extended appropriately for recommender systems, and d ) the feasibility and implications of meaningful applications of influence Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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