Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations

49 Pages Posted: 2 Oct 2020 Last revised: 7 Oct 2021

See all articles by Xuan Bi

Xuan Bi

University of Minnesota - Twin Cities - Carlson School of Management

Mochen Yang

University of Minnesota - Twin Cities - Carlson School of Management

Gediminas Adomavicius

University of Minnesota - Twin Cities - Carlson School of Management

Date Written: October 6, 2021

Abstract

We consider a marketplace where a recommender system provider (the firm) offers incentives to acquire prospective consumers, by leveraging information that a market intermediary collects about these consumers. We investigate a model of consumer acquisition that incorporates several factors affecting acquisition decisions, including the value that a consumer contributes to the recommender system, the cost of participation to the consumer (e.g., privacy loss), as well as the value that a consumer can derive from the system due to network externality created by existing consumers. Our model is dynamic in nature, where the firm iteratively decides the next acquisition target based on previously realized acquisition outcomes. We propose flexible data-driven procedures to estimate some of the key parameters in the model using consumers' data collected by the market intermediary, e.g., their historical consumption data or the consumption data of other similar consumers. We also design an algorithm to compute the dynamic acquisition sequence and the corresponding incentives to offer. We conduct simulation-based empirical evaluations on two canonical recommendation tasks: movie recommendation based on numerical ratings and product offer recommendation based on browsing (clicking) behaviors, and benchmark our acquisition model with random acquisition sequences with respect to (i) firm utility, (ii) recommender system performance, and (iii) consumer surplus. We find nuanced relationships between the firm's choice of incentive strategies and acquisition outcomes. Specifically, neither a constant strategy (setting the same incentive for all consumers) nor a fully greedy strategy (extracting all cumulative network externality) is optimal on all acquisition outcomes. Under a moderately greedy strategy, where the firm only partially extracts the cumulative network externality from consumers, the dynamic acquisition sequence can outperform random sequences on three acquisition outcomes simultaneously. Our work contributes a novel theoretical framework, practical insights, and design artifacts to facilitate effective consumer acquisition in recommender systems.

Keywords: consumer acquisition, recommender systems, algorithm design, network externality, personalization

Suggested Citation

Bi, Xuan and Yang, Mochen and Adomavicius, Gediminas, Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations (October 6, 2021). Available at SSRN: https://ssrn.com/abstract=3675644 or http://dx.doi.org/10.2139/ssrn.3675644

Xuan Bi

University of Minnesota - Twin Cities - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
United States

Mochen Yang (Contact Author)

University of Minnesota - Twin Cities - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
United States

Gediminas Adomavicius

University of Minnesota - Twin Cities - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
United States

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