research-article

Build Your Own Bundle - A Neural Combinatorial Optimization Method

Authors:

Changjie FanAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 2625 - 2633

https://doi.org/10.1145/3474085.3475440

Published: 17 October 2021 Publication History

Abstract

In the business domain,bundling is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in, such as the considerable research work on collaborative filtering that directly models the interaction between users and items. In this paper, we target at a practical but less explored recommendation problem named personalized bundle composition, which aims to offer an optimal bundle (i.e., a combination of items) to the target user. To tackle this specific recommendation problem, we formalize it as a combinatorial optimization problem on a set of candidate items and solve it within a neural combinatorial optimization framework. Extensive experiments on public datasets are conducted to demonstrate the superiority of the proposed method.

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Cited By

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
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
Liang Y(2024)Outlier item detection in bundle recommendation via the attention mechanismHigh-Confidence Computing10.1016/j.hcc.2024.1002004:3(100200)Online publication date: Sep-2024
https://doi.org/10.1016/j.hcc.2024.100200
Show More Cited By

Index Terms

Build Your Own Bundle - A Neural Combinatorial Optimization Method
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank
    2. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Copyright © 2021 ACM.

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New York, NY, United States

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Published: 17 October 2021

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MM '21: ACM Multimedia Conference

October 20 - 24, 2021

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Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

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
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
Liang Y(2024)Outlier item detection in bundle recommendation via the attention mechanismHigh-Confidence Computing10.1016/j.hcc.2024.1002004:3(100200)Online publication date: Sep-2024
https://doi.org/10.1016/j.hcc.2024.100200
Benamara IViennet E(2023)Contextual Advertising Strategy Generation via Attention and Interaction Guidance2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302533(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302533
Zhu SShen QCui CJi YZhang YDong ZWei Z(2023)Data-Augmented Counterfactual Learning for Bundle RecommendationDatabase Systems for Advanced Applications. DASFAA 2023 International Workshops10.1007/978-3-031-35415-1_22(314-330)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-35415-1_22

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