research-article

BRUCE: Bundle Recommendation Using Contextualized item Embeddings

Authors:

Tzoof Avny Brosh,

Oren Sar Shalom,

Bracha Shapira,

Mark LastAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 237 - 245

https://doi.org/10.1145/3523227.3546754

Published: 13 September 2022 Publication History

Abstract

A bundle is a pre-defined set of items that are collected together. In many domains, bundling is one of the most important marketing strategies for item promotion, commonly used in e-commerce. Bundle recommendation resembles the item recommendation task, where bundles are the recommended unit, but it poses additional challenges; while item recommendation requires only user and item understanding, bundle recommendation also requires modeling the connections between the various items in a bundle. Transformers have driven the state-of-the-art methods for set and sequence modeling in various natural language processing and computer vision tasks, emphasizing the understanding that the neighbors of an element are of crucial importance. Under some required adjustments, we believe the same applies for items in bundles, and better capturing the relations of an item with other items in the bundle may lead to improved recommendations. To address that, we introduce BRUCE - a novel model for bundle recommendation, in which we adapt Transformers to represent data on users, items, and bundles. This allows exploiting the self-attention mechanism to model the following: latent relations between the items in a bundle; and users’ preferences toward each of the items in the bundle and toward the whole bundle. Moreover, we examine various architectures to integrate the items’ and the users’ information and provide insights on architecture selection based on data characteristics. Experiments conducted on three benchmark datasets show that the proposed approach contributes to the accuracy of the recommendation and substantially outperforms state-of-the-art methods

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Cited By

Zhang PNiu ZMa RZhang F(2025)Multi-view graph contrastive representation learning for bundle recommendationInformation Processing & Management10.1016/j.ipm.2024.10395662:1(103956)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103956
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: 5-Jun-2024
https://dl.acm.org/doi/10.1145/3652865
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
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Index Terms

BRUCE: Bundle Recommendation Using Contextualized item Embeddings
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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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

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Published: 13 September 2022

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September 18 - 23, 2022

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Cited By

Zhang PNiu ZMa RZhang F(2025)Multi-view graph contrastive representation learning for bundle recommendationInformation Processing & Management10.1016/j.ipm.2024.10395662:1(103956)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103956
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: 5-Jun-2024
https://dl.acm.org/doi/10.1145/3652865
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.2023.332917536:7(3556-3567)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2023.3329175
Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1109/MCI.2024.3363984
De Biasio ANavarin NJannach D(2024)Economic recommender systems – a systematic reviewElectronic Commerce Research and Applications10.1016/j.elerap.2023.10135263:COnline publication date: 17-Apr-2024
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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
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Ariannezhad MLi MJullien Sde Rijke MChen HDuh WHuang HKato MMothe JPoblete B(2023)Complex Item Set RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3594248(3444-3447)Online publication date: 19-Jul-2023
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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
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