View in EDS HTML Full Text PDF Full Text

Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment.

Saved in:
Bibliographic Details
Title: Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment.
Authors: Sun, Ruixuan (AUTHOR), Akella, Avinash (AUTHOR), Kong, Ruoyan (AUTHOR), Zhou, Moyan (AUTHOR), Konstan, Joseph A. (AUTHOR)
Source: International Journal of Human-Computer Interaction. Nov2024, Vol. 40 Issue 22, p7233-7247. 15p.
Subjects: Recommender systems, Field research, Satisfaction
Abstract: Recommender systems often struggle to strike a balance between matching users' tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cover the full range of users' preferences, the system is perceived as useless. Conversely, when the system suggests too many items that users don't like, it is considered impersonal or ineffective. To better understand user sentiment about the breadth of recommendations given by a movie recommender, we conducted interviews and surveys and found out that many users considered narrow recommendations to be useful, while a smaller number explicitly wanted greater breadth. Additionally, we designed and ran an online field experiment with a larger user group, evaluating two new interfaces designed to provide users with greater access to broader recommendations. We looked at user preferences and behavior for two groups of users: those with higher initial movie diversity and those with lower diversity. Among our findings, we discovered that different levels of exploration control and users' subjective preferences on interfaces are more predictive of their satisfaction with the recommender. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd 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: Psychology and Behavioral Sciences Collection
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 180919778
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Sun%2C+Ruixuan%22">Sun, Ruixuan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Akella%2C+Avinash%22">Akella, Avinash</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kong%2C+Ruoyan%22">Kong, Ruoyan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Moyan%22">Zhou, Moyan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Konstan%2C+Joseph+A%2E%22">Konstan, Joseph A.</searchLink> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Nov2024, Vol. 40 Issue 22, p7233-7247. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Field+research%22">Field research</searchLink><br /><searchLink fieldCode="DE" term="%22Satisfaction%22">Satisfaction</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Recommender systems often struggle to strike a balance between matching users' tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cover the full range of users' preferences, the system is perceived as useless. Conversely, when the system suggests too many items that users don't like, it is considered impersonal or ineffective. To better understand user sentiment about the breadth of recommendations given by a movie recommender, we conducted interviews and surveys and found out that many users considered narrow recommendations to be useful, while a smaller number explicitly wanted greater breadth. Additionally, we designed and ran an online field experiment with a larger user group, evaluating two new interfaces designed to provide users with greater access to broader recommendations. We looked at user preferences and behavior for two groups of users: those with higher initial movie diversity and those with lower diversity. Among our findings, we discovered that different levels of exploration control and users' subjective preferences on interfaces are more predictive of their satisfaction with the recommender. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd 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://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=180919778
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/10447318.2023.2262796
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 7233
    Subjects:
      – SubjectFull: Recommender systems
        Type: general
      – SubjectFull: Field research
        Type: general
      – SubjectFull: Satisfaction
        Type: general
    Titles:
      – TitleFull: Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Sun, Ruixuan
      – PersonEntity:
          Name:
            NameFull: Akella, Avinash
      – PersonEntity:
          Name:
            NameFull: Kong, Ruoyan
      – PersonEntity:
          Name:
            NameFull: Zhou, Moyan
      – PersonEntity:
          Name:
            NameFull: Konstan, Joseph A.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 10447318
          Numbering:
            – Type: volume
              Value: 40
            – Type: issue
              Value: 22
          Titles:
            – TitleFull: International Journal of Human-Computer Interaction
              Type: main
ResultId 1