Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations
提出动态消费者获取模型，考虑消费者对推荐系统的价值、参与成本和网络外部性，通过数据驱动方法估计模型，并在三个数据集上模拟评估，发现适度贪婪的激励策略能同时提升企业效用、推荐系统性能和消费者剩余。
How to acquire the most valuable consumers to grow your recommender system? We propose a dynamic consumer acquisition model to enable value-driven acquisition decisions. We build a model of consumer acquisition that takes into account the value that a consumer contributes to the recommender system, the cost of their participation (e.g., privacy loss), and the value of their participation to other consumers (via network externality). We also propose data-driven procedures to estimate this model to enable informed, value-driven acquisition decisions. On three different data sets, we perform comprehensive simulation-based evaluations to demonstrate the performance of this dynamic consumer acquisition model. We find nuanced relationships between the firm’s choice of incentive strategies and acquisition outcomes. Neither a constant pricing strategy nor a greedy pricing strategy may be optimal. Instead, under a moderately greedy strategy, where the firm only partially extracts the network externality from consumers, the dynamic acquisition sequence can outperform random acquisition sequences on firm utility, recommender system performance, and consumer surplus simultaneously. Our work contributes a novel theoretical framework, practical insights, and design artifacts to facilitate effective consumer acquisition in recommender systems.