Sequential dynamic event recommendation in event-based social networks: An upper confidence bound approach

Liang, Yuan; Huang, Chuanjun; Bao, Xiuguo; Xu, Ke

doi:10.1016/j.ins.2020.06.047

Cited by 18 publications

(14 citation statements)

References 21 publications

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“…In addition, Ferreira et al [28] maximize the number of consumers who engage with the site. Liang et al [40] aim to maximize the revenue in the multiplepurchase setting while they assume that consumers always continue viewing the product list after purchasing any product, which is not practical in real scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…Many works [5,9,11,10,24] have been proposed to deal with demand learning or price experimentation problems with the help of the framework in the field of revenue management. Recently, more methods [40,17,13,14,6,44] adopt similar techniques in the product ranking setting, which inspires the algorithm proposed in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…However, most of them assume that each consumer purchases at most one product and stop browsing immediately afterward, which does not agree with the common practice since a consumer may expect multiple purchases on a website or an app [34,35,16,28]. Recently, several works consider a multiplepurchase setting while their targets are not revenue maximization [28,15], or they assume that consumers always continue viewing the product list after purchasing any product, which is not practical in real scenarios [40].…”

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%

“…Specifically, we constrain each consumer with a purchase budget to determine the maximal amount of purchases. Both the attention span and the purchase budget are assumed to obey the geometric distribution [13,40].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Related Workmentioning

confidence: 99%