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Research Abstracts Online
January - December 2011

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University of Minnesota Twin Cities
College of Science and Engineering
Department of Computer Science and Engineering

PI: Mohamed F. Mokbel

RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures

These researchers are developing a comprehensive experimental comparison of three recommender system architectures. They create a set of benchmark tasks based on the needs of a typical recommender-powered e-commerce site. They then evaluate the performance of the “hand-built” MultiLens recommender system, a DBMS-based recommender, and a data-stream management system. They employ two non-trivial data sets in this study: the 10 million rating MovieLens data, and the 100 million rating dataset used in the Netflix challenge. This study is the first of its kind.

Group Members

Ahmed El-Dawy, Graduate Student
Justin Lavandoski, Graduate Student
Mohamed Sawwat, Graduate Student