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Buy It Again: Modeling Repeat Purchase Recommendations

Authors:

Srevatsan Muralidharan,

Alex Lobzhanidze,

Shankar VishwanathAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 62 - 70

https://doi.org/10.1145/3219819.3219891

Published: 19 July 2018 Publication History

Abstract

Repeat purchasing, i.e., a customer purchasing the same product multiple times, is a common phenomenon in retail. As more customers start purchasing consumable products (e.g., toothpastes, diapers, etc.) online, this phenomenon has also become prevalent in e-commerce. However, in January 2014, when we looked at popular e-commerce websites, we did not find any customer-facing features that recommended products to customers from their purchase history to promote repeat purchasing. Also, we found limited research about repeat purchase recommendations and none that deals with the large scale purchase data that e-commerce websites collect. In this paper, we present the approach we developed for modeling repeat purchase recommendations. This work has demonstrated over 7% increase in the product click through rate on the personalized recommendations page of the Amazon.com website and has resulted in the launch of several customer-facing features on the Amazon.com website, the Amazon mobile app, and other Amazon websites.

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Cited By

El-Shaer EMcKee GHamdy A(2024)The LSTM-EMPG Model for Next Basket Recommendation in E-commerceInternational Journal of Information and Communication Sciences10.11648/j.ijics.20240901.129:1(9-23)Online publication date: 15-Jul-2024
https://doi.org/10.11648/j.ijics.20240901.12
Katz OBarkan OKoenigstein N(2024)Personalized Cadence Awareness for Next Basket RecommendationACM Transactions on Recommender Systems10.1145/36528633:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3652863
De TSingh PPatel A(2024)A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerceProceedings of the 2024 8th International Conference on Machine Learning and Soft Computing10.1145/3647750.3647754(17-24)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3647750.3647754
Show More Cited By

Index Terms

Buy It Again: Modeling Repeat Purchase Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web applications
      1. Electronic commerce
    2. Web searching and information discovery
      1. Personalization

Recommendations

Personalized Category Frequency prediction for Buy It Again recommendations
RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Buy It Again (BIA) recommendations are crucial to retailers to help improve user experience and site engagement by suggesting items that customers are likely to buy again based on their own repeat purchasing patterns. Most existing BIA studies analyze ...
The interplay of online shopping motivations and experiential factors on personalized e-commerce

This study uses complexity theory and configurational analysis to explain online shopping behavior.Online shopping experience and online shopping motivations combine to predict high purchase intention.Price sensitivity and promotion sensitivity are the ...
Developing recommender systems with the consideration of product profitability for sellers

In electronic commerce web sites, recommender systems are popularly being employed to help customers in selecting suitable products to meet their personal needs. These systems learn about user preferences over time and automatically suggest products ...

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Published In

cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2018

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KDD '18

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

El-Shaer EMcKee GHamdy A(2024)The LSTM-EMPG Model for Next Basket Recommendation in E-commerceInternational Journal of Information and Communication Sciences10.11648/j.ijics.20240901.129:1(9-23)Online publication date: 15-Jul-2024
https://doi.org/10.11648/j.ijics.20240901.12
Katz OBarkan OKoenigstein N(2024)Personalized Cadence Awareness for Next Basket RecommendationACM Transactions on Recommender Systems10.1145/36528633:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3652863
De TSingh PPatel A(2024)A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerceProceedings of the 2024 8th International Conference on Machine Learning and Soft Computing10.1145/3647750.3647754(17-24)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3647750.3647754
Ban GYun HLee BSung DWoo SSerra ESpezzano F(2024)Deep Journey Hierarchical Attention Networks for Conversion Predictions in Digital MarketingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680066(4358-4365)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680066
Park RPande ARelyea DChennu PKanmanth Reddy PHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)SLH-BIA: Short-Long Hawkes Process for Buy It Again Recommendations at ScaleProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661374(2965-2969)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661374
Zhu YChen JChen LLi YZhang FLiu ZHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Interest Clock: Time Perception in Real-Time Streaming Recommendation SystemProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661369(2915-2919)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661369
Chang HAgarwal NMcCallum AAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential RecommendersProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635755(67-76)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635755
Wang ZWei WFeng SMao XQiu MChen DFang R(2024)Exploiting Group-Level Behavior Pattern for Session-Based RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328031036:1(152-166)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3280310
Suh Y(2024)Repurchase Prediction Using Survival Ensembles in CRM Systems for Home Appliance BusinessIEEE Access10.1109/ACCESS.2024.343764812(107201-107218)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3437648
Pande AGhosh KPark R(2023)Personalized Category Frequency prediction for Buy It Again recommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608822(730-736)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608822
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