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MovieLens: Filtering on Others' Reactions
Lichtendahl, Kenneth C. Jr.; Boatright, Benjamin; Holtz, Paul Case QA-0927 / Published August 17, 2021 / 6 pages.
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Product Overview

This case introduces students to relational databases and the basics of collaborative filtering, a popular recommendation system technique. The case points to a dataset shared by GroupLens, a research group developing algorithms that supply movie recommendations through a website called MovieLens. SQL queries provided in the case propose a collaborative filtering strategy for generating simple recommendations with a small degree of personalization. The basic method offered in the case opens the door for students to improve the supplied queries or develop a more elaborate algorithm altogether for generating recommendations.


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  • Overview

    This case introduces students to relational databases and the basics of collaborative filtering, a popular recommendation system technique. The case points to a dataset shared by GroupLens, a research group developing algorithms that supply movie recommendations through a website called MovieLens. SQL queries provided in the case propose a collaborative filtering strategy for generating simple recommendations with a small degree of personalization. The basic method offered in the case opens the door for students to improve the supplied queries or develop a more elaborate algorithm altogether for generating recommendations.

  • Learning Objectives