research-article

Bundle recommendation in ecommerce

Authors:

Patrick Harrington,

Lei TangAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 657 - 666

https://doi.org/10.1145/2600428.2609603

Published: 03 July 2014 Publication History

Abstract

Recommender system has become an important component in modern eCommerce. Recent research on recommender systems has been mainly concentrating on improving the relevance or profitability of individual recommended items. But in reality, users are usually exposed to a set of items and they may buy multiple items in one single order. Thus, the relevance or profitability of one item may actually depend on the other items in the set. In other words, the set of recommendations is a bundle with items interacting with each other. In this paper, we introduce a novel problem called the Bundle Recommendation Problem (BRP). By solving the BRP, we are able to find the optimal bundle of items to recommend with respect to preferred business objective. However, BRP is a large-scale NP-hard problem. We then show that it may be sufficient to solve a significantly smaller version of BRP depending on properties of input data. This allows us to solve BRP in real-world applications with millions of users and items. Both offline and online experimental results on a Walmart.com demonstrate the incremental value of solving BRP across multiple baseline models.

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Cited By

Roumeliotis KTselikas NNasiopoulos D(2024)Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender SystemsSoftware10.3390/software30100043:1(62-80)Online publication date: 29-Feb-2024
https://doi.org/10.3390/software3010004
Sun ZFeng KYang JFang HQu XOng YLiu W(2024)Revisiting Bundle Recommendation for Intent-aware Product BundlingACM Transactions on Recommender Systems10.1145/36528652:3(1-34)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3652865
Li MLi LTao XHuang JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and HealthinessProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657857(564-574)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657857
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Index Terms

Bundle recommendation in ecommerce

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cover image ACM Conferences

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Copyright © 2014 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

Publication History

Published: 03 July 2014

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SIGIR '14

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SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Roumeliotis KTselikas NNasiopoulos D(2024)Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender SystemsSoftware10.3390/software30100043:1(62-80)Online publication date: 29-Feb-2024
https://doi.org/10.3390/software3010004
Sun ZFeng KYang JFang HQu XOng YLiu W(2024)Revisiting Bundle Recommendation for Intent-aware Product BundlingACM Transactions on Recommender Systems10.1145/36528652:3(1-34)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3652865
Li MLi LTao XHuang JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and HealthinessProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657857(564-574)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657857
Sun ZFeng KYang JQu XFang HOng YLiu WHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Adaptive In-Context Learning with Large Language Models for Bundle GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657808(966-976)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657808
Zou DZhao SWei WMao XLi RChen DFang RFu Y(2024)Towards Hierarchical Intent Disentanglement for Bundle RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3329175(1-12)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3329175
Yang WYang CLi JTan YLu XShi C(2024)Non-autoregressive personalized bundle generationInformation Processing & Management10.1016/j.ipm.2024.10381461:5(103814)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103814
Liu XYuan M(2023)RETRACTED: A Global Structural Hypergraph Convolutional Model for Bundle RecommendationElectronics10.3390/electronics1218395212:18(3952)Online publication date: 19-Sep-2023
https://doi.org/10.3390/electronics12183952
Li MLi LTao XZhong N(2023)User-Meal Interaction Learning for Meal Recommendation: A Reproducibility StudyProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625342(104-113)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625342
Bibas KSar Shalom OJannach D(2023)Semi-supervised Adversarial Learning for Complementary Item RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583462(1804-1812)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583462
Ren YHaonan ZFu LWang XZhou CChen HDuh WHuang HKato MMothe JPoblete B(2023)Distillation-Enhanced Graph Masked Autoencoders for Bundle RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591666(1660-1669)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591666
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