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Research Abstracts Online
January 2010 - March 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

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