View in EDS

IPGAN: Generating Informative Item Pairs by Adversarial Sampling.

Saved in:
Bibliographic Details
Title: IPGAN: Generating Informative Item Pairs by Adversarial Sampling.
Authors: Guo, Guibing1 (AUTHOR), Zhou, Huan1 (AUTHOR) zhouh2irv@gmail.com, Chen, Bowei1 (AUTHOR) boweichen_public@outlook.com, Liu, Zhirong2 (AUTHOR), Xu, Xiao1 (AUTHOR), Chen, Xu3 (AUTHOR), Dong, Zhenhua2 (AUTHOR), He, Xiuqiang2 (AUTHOR)
Source: IEEE Transactions on Neural Networks & Learning Systems. Feb2022, Vol. 33 Issue 2, p694-706. 13p.
Subject Terms: GENERATIVE adversarial networks, BINARY codes, GALLIUM nitride
Abstract: Negative sampling plays an important role in ranking-based recommender models. However, most existing sampling methods cannot generate informative item pairs with positive and negative instances due to two limitations: 1) they merely treat observed items as positive instances, ignoring the existence of potential positive items (i.e., nonobserved items users may prefer) and the probability of observed but noisy items and 2) they fail to capture the relationship between positive and negative items during negative sampling, which may cause the unexpected selection of potential positive items. In this article, we introduce a dynamic sampling strategy to search informative item pairs. Specifically, we first sample a positive instance from all the items by leveraging the overall features of user’s observed items. Then, we strategically select a negative instance by considering its correlation with the sampled positive one. Formally, we propose an item pair generative adversarial network named IPGAN, where our sampling strategy is realized in two generative models for positive and negative instances, respectively. In addition, IPGAN can also ensure that the sampled item pairs are informative relative to the ground truth by a discriminative model. What is more, we propose a batch-training approach to further enhance both user and item modeling by alleviating the special bias (noise) from different users. This approach can also significantly accelerate the process of model training compared with classical GAN method for recommendation. Experimental results on three real data sets show that our approach outperforms other state-of-the-art approaches in terms of recommendation accuracy. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Business Source Index
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:bsx&genre=article&issn=2162237X&ISBN=&volume=33&issue=2&date=20220201&spage=694&pages=694-706&title=IEEE Transactions on Neural Networks & Learning Systems&atitle=IPGAN%3A%20Generating%20Informative%20Item%20Pairs%20by%20Adversarial%20Sampling.&aulast=Guo%2C%20Guibing&id=DOI:10.1109/TNNLS.2020.3028572
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Guo%20G
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: bsx
DbLabel: Business Source Index
An: 155108521
RelevancyScore: 1251
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 1251.41735839844
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: IPGAN: Generating Informative Item Pairs by Adversarial Sampling.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Guo%2C+Guibing%22">Guo, Guibing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Huan%22">Zhou, Huan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhouh2irv@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Bowei%22">Chen, Bowei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> boweichen_public@outlook.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zhirong%22">Liu, Zhirong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Xiao%22">Xu, Xiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Xu%22">Chen, Xu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Zhenhua%22">Dong, Zhenhua</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Xiuqiang%22">He, Xiuqiang</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Neural+Networks+%26+Learning+Systems%22">IEEE Transactions on Neural Networks & Learning Systems</searchLink>. Feb2022, Vol. 33 Issue 2, p694-706. 13p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22GENERATIVE+adversarial+networks%22">GENERATIVE adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22BINARY+codes%22">BINARY codes</searchLink><br /><searchLink fieldCode="DE" term="%22GALLIUM+nitride%22">GALLIUM nitride</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Negative sampling plays an important role in ranking-based recommender models. However, most existing sampling methods cannot generate informative item pairs with positive and negative instances due to two limitations: 1) they merely treat observed items as positive instances, ignoring the existence of potential positive items (i.e., nonobserved items users may prefer) and the probability of observed but noisy items and 2) they fail to capture the relationship between positive and negative items during negative sampling, which may cause the unexpected selection of potential positive items. In this article, we introduce a dynamic sampling strategy to search informative item pairs. Specifically, we first sample a positive instance from all the items by leveraging the overall features of user’s observed items. Then, we strategically select a negative instance by considering its correlation with the sampled positive one. Formally, we propose an item pair generative adversarial network named IPGAN, where our sampling strategy is realized in two generative models for positive and negative instances, respectively. In addition, IPGAN can also ensure that the sampled item pairs are informative relative to the ground truth by a discriminative model. What is more, we propose a batch-training approach to further enhance both user and item modeling by alleviating the special bias (noise) from different users. This approach can also significantly accelerate the process of model training compared with classical GAN method for recommendation. Experimental results on three real data sets show that our approach outperforms other state-of-the-art approaches in terms of recommendation accuracy. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsx&AN=155108521
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/TNNLS.2020.3028572
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 694
    Subjects:
      – SubjectFull: GENERATIVE adversarial networks
        Type: general
      – SubjectFull: BINARY codes
        Type: general
      – SubjectFull: GALLIUM nitride
        Type: general
    Titles:
      – TitleFull: IPGAN: Generating Informative Item Pairs by Adversarial Sampling.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Guo, Guibing
      – PersonEntity:
          Name:
            NameFull: Zhou, Huan
      – PersonEntity:
          Name:
            NameFull: Chen, Bowei
      – PersonEntity:
          Name:
            NameFull: Liu, Zhirong
      – PersonEntity:
          Name:
            NameFull: Xu, Xiao
      – PersonEntity:
          Name:
            NameFull: Chen, Xu
      – PersonEntity:
          Name:
            NameFull: Dong, Zhenhua
      – PersonEntity:
          Name:
            NameFull: He, Xiuqiang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2022
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 2162237X
          Numbering:
            – Type: volume
              Value: 33
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: IEEE Transactions on Neural Networks & Learning Systems
              Type: main
ResultId 1