Highly Recommended: Collaborative Filtering Gives Customers What They Want

11 Pages Posted: 4 Sep 2019

See all articles by Rajkumar Venkatesan

Rajkumar Venkatesan

University of Virginia - Darden School of Business

Shea Gibbs

University of Virginia - Darden School of Business

Abstract

Netflix Top Picks, Amazon recommendations, the iTunes Genius button. They all have one thing in common: they are driven by clever algorithms that use a technique known as collaborative filtering. Often used in machine learning operations, collaborative filtering is the process by which a firm like Netflix generates predictions about a single user's preferences using data taken from a large number of users. This technical note offers an overview of three of the main collaborative filtering methods: slope one, a purely predictive nonparametric model; ordinal logit, a parametric regression model; and alternative least squares, a matrix factorization technique.

Excerpt

UVA-M-0974

Aug. 23, 2019

Highly Recommended:

Collaborative Filtering Gives Customers What They Want

You're ready to Netflix and chill. You pull up your browser and scroll through the new releases on Netflix.com. Nothing looks interesting. You turn your attention to the recommended titles, the “Top Picks” selected just for you. And there it is—the classic film you'd forgotten you wanted to see but has always been at the top of your to-watch list.

Netflix Top Picks, Amazon recommendations, the iTunes Genius button. They all have one thing in common: they are driven by clever algorithms that use a technique known as collaborative filtering.

. . .

Keywords: collaborative filtering, slope one, parametric model, nonparametric model, logistic regression, weighted averages, predictive analytics, ordinal logit, recommendation algorithms, choice behavior, decision analysis, proportional odds assumption, alternative least squares, cold start problem, popularity bias, matrix factorization, streaming video, marketing analytics, data analysis, Netflix, cluster analysis

Suggested Citation

Venkatesan, Rajkumar and Gibbs, Shea, Highly Recommended: Collaborative Filtering Gives Customers What They Want. Darden Case No. UVA-M-0974. Available at SSRN: https://ssrn.com/abstract=3445953

Rajkumar Venkatesan (Contact Author)

University of Virginia - Darden School of Business

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

Shea Gibbs

University of Virginia - Darden School of Business

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

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