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Accurate bundle matching and generation via multitask learning with partially shared parameters

How can we recommend existing bundles to users accurately? How can we generate new tailored bundles for users? Recommending a bundle, or a group of various items, has attracted widespread attention in e-commerce owing to the increased satisfaction of both users and providers. Bundle matching and bun...

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Detalles Bibliográficos
Autores principales: Jeon, Hyunsik, Jang, Jun-Gi, Kim, Taehun, Kang, U.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019671/
https://www.ncbi.nlm.nih.gov/pubmed/36928193
http://dx.doi.org/10.1371/journal.pone.0280630
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author Jeon, Hyunsik
Jang, Jun-Gi
Kim, Taehun
Kang, U.
author_facet Jeon, Hyunsik
Jang, Jun-Gi
Kim, Taehun
Kang, U.
author_sort Jeon, Hyunsik
collection PubMed
description How can we recommend existing bundles to users accurately? How can we generate new tailored bundles for users? Recommending a bundle, or a group of various items, has attracted widespread attention in e-commerce owing to the increased satisfaction of both users and providers. Bundle matching and bundle generation are two representative tasks in bundle recommendation. The bundle matching task is to correctly match existing bundles to users while the bundle generation is to generate new bundles that users would prefer. Although many recent works have developed bundle recommendation models, they fail to achieve high accuracy since they do not handle heterogeneous data effectively and do not learn a method for customized bundle generation. In this paper, we propose BundleMage, an accurate approach for bundle matching and generation. BundleMage effectively mixes user preferences of items and bundles using an adaptive gate technique to achieve high accuracy for the bundle matching. BundleMage also generates a personalized bundle by learning a generation module that exploits a user preference and the characteristic of a given incomplete bundle to be completed. BundleMage further improves its performance using multi-task learning with partially shared parameters. Through extensive experiments, we show that BundleMage achieves up to 6.6% higher nDCG in bundle matching and 6.3× higher nDCG in bundle generation than the best competitors. We also provide qualitative analysis that BundleMage effectively generates bundles considering both the tastes of users and the characteristics of target bundles.
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spelling pubmed-100196712023-03-17 Accurate bundle matching and generation via multitask learning with partially shared parameters Jeon, Hyunsik Jang, Jun-Gi Kim, Taehun Kang, U. PLoS One Research Article How can we recommend existing bundles to users accurately? How can we generate new tailored bundles for users? Recommending a bundle, or a group of various items, has attracted widespread attention in e-commerce owing to the increased satisfaction of both users and providers. Bundle matching and bundle generation are two representative tasks in bundle recommendation. The bundle matching task is to correctly match existing bundles to users while the bundle generation is to generate new bundles that users would prefer. Although many recent works have developed bundle recommendation models, they fail to achieve high accuracy since they do not handle heterogeneous data effectively and do not learn a method for customized bundle generation. In this paper, we propose BundleMage, an accurate approach for bundle matching and generation. BundleMage effectively mixes user preferences of items and bundles using an adaptive gate technique to achieve high accuracy for the bundle matching. BundleMage also generates a personalized bundle by learning a generation module that exploits a user preference and the characteristic of a given incomplete bundle to be completed. BundleMage further improves its performance using multi-task learning with partially shared parameters. Through extensive experiments, we show that BundleMage achieves up to 6.6% higher nDCG in bundle matching and 6.3× higher nDCG in bundle generation than the best competitors. We also provide qualitative analysis that BundleMage effectively generates bundles considering both the tastes of users and the characteristics of target bundles. Public Library of Science 2023-03-16 /pmc/articles/PMC10019671/ /pubmed/36928193 http://dx.doi.org/10.1371/journal.pone.0280630 Text en © 2023 Jeon et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jeon, Hyunsik
Jang, Jun-Gi
Kim, Taehun
Kang, U.
Accurate bundle matching and generation via multitask learning with partially shared parameters
title Accurate bundle matching and generation via multitask learning with partially shared parameters
title_full Accurate bundle matching and generation via multitask learning with partially shared parameters
title_fullStr Accurate bundle matching and generation via multitask learning with partially shared parameters
title_full_unstemmed Accurate bundle matching and generation via multitask learning with partially shared parameters
title_short Accurate bundle matching and generation via multitask learning with partially shared parameters
title_sort accurate bundle matching and generation via multitask learning with partially shared parameters
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019671/
https://www.ncbi.nlm.nih.gov/pubmed/36928193
http://dx.doi.org/10.1371/journal.pone.0280630
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