Revenue Maximization and Learning in Products Ranking

Chen, Ningyuan; Li, Anran; Yang, Shuoguang

doi:10.1145/3465456.3467610

Cited by 5 publications

(16 citation statements)

References 3 publications

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“…Chen et al [6] consider the revenue maximization problem with unknown parameters and a cascading model for customer behavior. However, the decision variables in this work are still rankings of offered items, and the customer is assumed to purchase at most one item.…”

Section: Bandits and Sequential Recommendationsmentioning

confidence: 99%

Learning to Sell a Focal-ancillary Combination

Wang¹,

Li²,

Talluri³

2022

Preprint

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A number of products are sold in the following sequence: First a focal product is shown, and if the customer purchases, one or more ancillary products are displayed for purchase. A prominent example is the sale of an airline ticket, where first the flight is shown, and when chosen, a number of ancillaries such as cabin or hold bag options, seat selection, insurance etc. are presented. The firm has to decide on a sale format-whether to sell them in sequence unbundled, or together as a bundle-and how to price the focal and ancillary products, separately or as a bundle. Since the ancillary is considered by the customer only after the purchase of the focal product, the sale strategy chosen by the firm creates an information and learning dependency between the products: for instance, offering only a bundle would preclude learning customers' valuation for the focal and ancillary products individually. In this paper we study learning strategies for such focal and ancillary item combinations under the following scenarios: (a) pure unbundling to all customers, (b) personalized mechanism, where, depending on some observed features of the customers, the two products are presented and priced as a bundle or in sequence, (c) initially unbundling (for all customers), and switch to bundling (if more profitable) permanently once during the horizon. We design pricing and decisions algorithms for all three scenarios, with regret upper bounded by O(d √ T log T ), and an optimal switching time for the third scenario.

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Section: Bandits and Sequential Recommendationsmentioning

confidence: 99%

Learning to Sell a Focal-ancillary Combination

Wang¹,

Li²,

Talluri³

2022

Preprint

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Section: Introductionmentioning

confidence: 99%

“…Online retailing has become increasingly popular over the last decades [17,28,52]. The way of product ranking is the crux for online retailers because it determines the consumers' shopping behaviors [17] and thus influences the retailers' revenue [20,49]. For instance, the probability of consumers' purchasing from a firm or clicking an advertisement is strongly related to the display order [8,3,33].…”

Section: Introductionmentioning

confidence: 99%

“…The consumer choice model, when faced with a list of products, is the basis of the problem [17]. Generally speaking, existing works assume that the consumers view the list of products sequentially and select products according to a choice model [43,42,32,38,18,26,25,37].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose a more realistic consumer choice model to characterize consumer behaviors under multiple-purchase settings. Following [13,17,40], we assume that the consumers view the product list sequentially and purchase a product when its utility exceeds a certain threshold. Similar to [17], we assume the existence of the attention span for each consumer, which determines the maximal amount of products that the consumer is willing to view.…”

Section: Introductionmentioning

confidence: 99%

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Product Ranking for Revenue Maximization with Multiple Purchases

Xu¹,

Zhang²,

Li³

et al. 2022

Preprint

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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( √ 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. The source code is available at https://github.com/windxrz/MPB-UCB.

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