Prediction of O2O Coupon Usage Based on XGBoost Model

Shi, Qin

doi:10.1145/3414752.3414775

Cited by 5 publications

(7 citation statements)

References 6 publications

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“…Feature engineering plays an irreplaceable role in the model, and it is decisive to the final performance of model. It defines the upper limit of the model [3] .…”

Section: Feature Engineeringmentioning

confidence: 99%

“…When the missing value does not take up a large proportion, we can handle the missing value by filling data that related to the existing data. For instance, median, average number, or mode [3] .…”

Section: Feature Engineeringmentioning

confidence: 99%

“…Hence personalized delivery of coupons is a crucial technology for improving coupon redemption rates, as it enables consumers who have specific preferences to receive some benefits. And in terms of the benefits that merchants receive is to improve coupon verification rate, giving merchants stronger marketing ability, helping merchants' precision marketing, increase user stickiness by locating costumers precisely, and helping merchants to have reasonable and rational coupon distribution strategies [3] .…”

Section: Introductionmentioning

confidence: 99%

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Forecast of O2O Coupon Consumption Based on XGBoost Model

2023

AJCIS

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A precise delivery of coupon is an important way to engage existing customers or attract new ones to physical stores in O2O marketing approach. And a suitable strategy of coupon distribution can significantly heighten the user experience and facilitate coupon re-consumption. In this paper, we design a prediction model of O2O coupon usage based on XGBoost and compare the performance of XGBoost with another model based on the average AUC value. By contrast, the XGBoost performs better than the other model with 0.9584 average AUC value. So this model can help merchants to locate the target accurately.

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“…Feature engineering plays an irreplaceable role in the model, and it is decisive to the final performance of model. It defines the upper limit of the model [3] .…”

Section: Feature Engineeringmentioning

confidence: 99%

Section: Feature Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Forecast of O2O Coupon Consumption Based on XGBoost Model

2023

AJCIS

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“…For the first type, most of the studies adopt the CTR models widely utilized in item-wise recommendation. In [28], XGBoost model is used to solve the Online-to-Offline coupon redemption rate prediction, based on both user profile and coupon characteristic features. In [20], a sequence-based structure with attention-based mechanism is deployed, which considers long-term and short-term user behavior in addition to the voucher information.…”

Section: Related Work 21 Voucher-related Workmentioning

confidence: 99%

“…There are some preliminary studies [15,20,28] focusing on VRR prediction. Those studies solve voucher redemption rate prediction tasks by directly borrowing the ideas from user-item Click-Through-Rate (CTR) prediction models [6,38].…”

Section: Introductionmentioning

confidence: 99%

DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction

Xiao¹,

Li²,

Xu³

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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In E-commerce, vouchers are important marketing tools to enhance users' engagement and boost sales and revenue. The likelihood that a user redeems a voucher is a key factor in voucher distribution decision. User-item Click-Through-Rate (CTR) models are often applied to predict the user-voucher redemption rate. However, the voucher scenario involves more complicated relations among users, items and vouchers. The users' historical behavior in a voucher collection activity reflects users' voucher usage patterns, which is nevertheless overlooked by the CTR-based solutions. In this paper, we propose a Deep Multi-behavior Graph Networks (DMBGN) to shed light on this field for the voucher redemption rate prediction. The complex structural user-voucher-item relationships are captured by a User-Behavior Voucher Graph (UVG). User behavior happening both before and after voucher collection is taken into consideration, and a high-level representation is extracted by Higher-order Graph Neural Networks. On top of a sequence of UVGs, an attention network is built which can help to learn users' long-term voucher redemption preference. Extensive experiments on three large-scale production datasets demonstrate the proposed DMBGN model is effective, with 10% to 16% relative AUC improvement over Deep Neural Networks (DNN), and 2% to 4% AUC improvement over Deep Interest Network (DIN). Source code and a sample dataset are made publicly available to facilitate future research 1 .

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Predicting Consumers’ Coupon-usage in E-commerce with Capsule Network

Jiang

Tan

et al. 2022

Algorithms and Architectures for Parallel Processing

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Prediction of O2O Coupon Usage Based on XGBoost Model

Cited by 5 publications

References 6 publications

Forecast of O2O Coupon Consumption Based on XGBoost Model

Forecast of O2O Coupon Consumption Based on XGBoost Model

DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction

Predicting Consumers’ Coupon-usage in E-commerce with Capsule Network

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