Product Ranking for Revenue Maximization with Multiple Purchases
This addresses revenue maximization for online retailers by modeling more realistic consumer behavior, though it is incremental as it extends existing choice models.
The paper tackles product ranking for online retailers by allowing consumers to purchase multiple products, unlike prior models that assume single purchases, and proposes an optimal policy and an online algorithm (MPB-UCB) with Õ(√T) regret, validated on synthetic and semi-synthetic datasets.
Product ranking is the core problem for revenue-maximizing online retailers. To design proper product ranking algorithms, various consumer choice models are proposed to characterize the consumers' behaviors when they are provided with a list of products. However, existing works assume that each consumer purchases at most one product or will keep viewing the product list after purchasing a product, which does not agree with the common practice in real scenarios. In this paper, we assume that each consumer can purchase multiple products at will. To model consumers' willingness to view and purchase, we set a random attention span and purchase budget, which determines the maximal amount of products that he/she views and purchases, respectively. Under this setting, we first design an optimal ranking policy when the online retailer can precisely model consumers' behaviors. Based on the policy, we further develop the Multiple-Purchase-with-Budget UCB (MPB-UCB) algorithms with $Õ(\sqrt{T})$ regret that estimate consumers' behaviors and maximize revenue simultaneously in online settings. Experiments on both synthetic and semi-synthetic datasets prove the effectiveness of the proposed algorithms.