John Benjamin Schafer

UNIVERSITY OF MINNESOTA This is to certify that I have examined this bound copy of a doctoral thesis by John Benjamin Schafer and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made. Joseph A. Konstan _______________________________________________________ Name of Faculty Advisor _______________________________________________________ Signature of Faculty Advisor _______________________________________________________ Date John T. Riedl _______________________________________________________ Name of Faculty Advisor _______________________________________________________ Signature of Faculty Advisor _______________________________________________________ Date GRADUATE SCHOOL MetaLens: A Framework for Multi-source Recommendations A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY John Benjamin Schafer IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY July 2001 John Benjamin Schafer 2001 i ACKNOWLEDGEMENTS This thesis was possible thanks to the help and support of many people. Thank you to my colleagues in the GroupLens research project and our predecessors. Specifically, thank you to Jon Herlocker for being the student leadership the group needed for so many years. Thank you to Dan Cosley, Nathan Good, Tony Lam, Sean McNee, and Badrul Sarwar for your support of and contributions to this research. Thank you to my co-advisors, mentors, and, yes, friends, Professors Joseph Konstan and John Riedl. Both in and out of the classroom, you have demonstrated that teaching and learning go hand-in-hand and can occur when you least expect it. As I continue my career, I will always remember the many lessons that you have taught. Thank you to the original Dr. Schafer – my father, John. Growing up, I learned what it means to be a teacher by watching you. If I enjoy my career only half as much as you did yours, I will be a rich man indeed. Thank you to the second Dr. Schafer – my brother, Joe. Your struggles with graduate school always occurred just far enough prior to mine that I recognized my own as simply part of the process. Thank you to my mother, Grace, the rest of the “educating” Schafers – Katie, Greg and Shelby – the Frohardts and the Olsons. Your love and support can’t be measured or repaid. I hope you know how deeply it was appreciated. Finally, thank you to “my girls.” To my beautiful Margaret, thank you for your radiant smile that always cheered me in the closing days of this degree and gave me the perspective that I desperately needed. To my even more beautiful wife, Amy, you didn’t complain once when we packed up and moved our lives to further my career (either in coming to Minneapolis or in leaving it). When the process seemed at its worst, that was when I could count most on your steadfast support. I owe you the world that you have made possible. ii ABSTRACT In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They do so by connecting users with information regarding the content of recommended items or the opinions of other individuals. In this thesis, we focus on a new class of recommender systems called meta- recommenders. Meta-recommender systems build on existing recommender technologies by giving users control over the combination of rich recommendation data to yield more personalized recommendations. The work presented in this thesis makes several significant contributions to the field of recommender systems. We begin by considering the technologies used in creating recommender systems and the variety of ways these technologies are applied and recommendations presented in e-commerce recommender applications. We use this information to create a taxonomy for recommender applications in e-commerce. We also consider correlations between the recommender application models used to recommend products and the sites that choose to implement them. Next, we introduce meta-recommenders and present the MetaLens Recommendation Framework. This framework serves as a model for how meta- recommenders collect data and generate recommendations that users find understandable, usable, and helpful. A series of controlled use experiments indicate that users want these systems to provide recommendation data alongside the recommendation. Furthermore, when appropriate, users want control over which data is displayed. Implementation studies show the development of three different recommender systems built within this framework. Analysis of public use of these systems demonstrates that users like, and often prefer, these systems to more “traditional” recommenders. While acceptance comes at a slow pace, users who customized a system were more likely to return to use the system again. Finally, while the quantity and type of recommendation data preferred varies widely from user to user, analysis demonstrates that users want access to as much recommendation data as possible. All told, these results provide a meaningful foundation for the design of future meta-recommenders. iii TABLE OF CONTENTS Chapter 1 : Introduction...................................................................................................... 1 1.1 Recommender Systems........................................................................................... 2 1.2 Meta-recommender Systems................................................................................... 3 1.3 Research Challenges............................................................................................... 4 1.3.1 Challenge 1: What format should meta-recommendations take? ................... 4 1.3.2 Challenge 2: Which interface do users prefer in a recommender system? ..... 4 1.3.3 Challenge 3: How do users interact with meta-recommender systems?......... 5 1.4 Research Methods................................................................................................... 5 1.5 Contributions........................................................................................................... 6 1.5.1 Chapter 3......................................................................................................... 6 1.5.2 Chapter 4......................................................................................................... 6 1.5.3 Chapter 5......................................................................................................... 7 1.5.4 Chapter 6......................................................................................................... 7 1.5.5 Chapter 7......................................................................................................... 7 1.6 Overview of Thesis................................................................................................. 7 Chapter 2 : Related Work ................................................................................................... 9 2.1 Electronic Commerce.............................................................................................. 9 2.1.1 How Sites Use Recommender Applications................................................. 10 2.1.2 Why Sites Use Recommender Applications................................................. 12 2.2 Information Filtering and Information Retrieval .................................................. 14 2.2.1 Information Retrieval.................................................................................... 14 2.2.2 Information Filtering..................................................................................... 16 2.2.3 Information Filtering and Retrieval Technology .......................................... 17 2.3 Data Mining.......................................................................................................... 18 2.4 Collaborative Filtering.......................................................................................... 21 2.5 Hybrid Systems..................................................................................................... 23 2.5.1 The Problem.................................................................................................. 23 2.5.2 The Solution.................................................................................................. 25 2.6 Meta-recommender Systems................................................................................. 28 Chapter 3 : A Taxonomy for Recommender Systems ...................................................... 30 3.1 Recommender Applications in Electronic Commerce.......................................... 31 3.1.1 Amazon.com ................................................................................................. 31 3.1.2 CDNOW ....................................................................................................... 33 3.1.3 drugstore.com................................................................................................ 35 3.1.4 eBay .............................................................................................................. 35 3.1.5 MovieFinder.com.......................................................................................... 36 3.1.6 Reel.com ....................................................................................................... 36 3.2 A Taxonomy ......................................................................................................... 40 3.2.1 Functional I/O..............................................................................................

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