Cognitive Load Modeling in Mobile Touch Interaction and Optimization of Marketing Information Presentation Strategies.
Saved in:
| Title: | Cognitive Load Modeling in Mobile Touch Interaction and Optimization of Marketing Information Presentation Strategies. |
|---|---|
| Authors: | Wang, Sufeng1, Hu, Lili1 hulili5015@163.com |
| Source: | International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 9, p80-94. 15p. |
| Subjects: | Cognitive load, Reinforcement learning, Decision making, Touch screens, Marketing, Human-computer interaction |
| Abstract: | In mobile touch interaction environments, a mismatch between marketing information presentation and users' cognitive load often results in suboptimal interaction experiences and low commercial conversion efficiency, thereby constraining the advancement of mobile marketing optimization. This study proposes an integrated technical framework that combines real-time multimodal cognitive load quantification with reinforcement learning-based adaptive decision-making to dynamically align marketing information presentation with users' real-time cognitive states. The framework consists of two core modules: a multimodal real-time cognitive load estimation model and a reinforcement learning-driven adaptive information presentation decision engine. The former synchronously collects multimodal data--including touch interaction behaviors, eye-tracking signals, and basic physiological indicators--and constructs a high-discriminability feature system integrated with a temporal multi-head attention fusion network. This design enables precise, millisecond-level cognitive load quantification without reliance on bulky laboratory equipment. The latter module treats cognitive load as the primary state signal, designs a multi-objective reward mechanism that balances shortterm user experience and long-term commercial value, and employs a cloud-edge collaborative deployment architecture to achieve dynamic and adaptive adjustment of marketing information presentation strategies. The two modules are tightly coupled through a real-time data streaming pipeline, effectively addressing the challenges of multimodal synchronization and low-latency decision-making in mobile environments. Experimental results in mobile marketing scenarios demonstrate that the proposed framework accurately captures users' real-time cognitive load, significantly optimizes information presentation effectiveness, and enhances both interaction experience and commercial conversion efficiency. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 193831335 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Cognitive Load Modeling in Mobile Touch Interaction and Optimization of Marketing Information Presentation Strategies. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Sufeng%22">Wang, Sufeng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Hu%2C+Lili%22">Hu, Lili</searchLink><relatesTo>1</relatesTo><i> hulili5015@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 9, p80-94. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Cognitive+load%22">Cognitive load</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Touch+screens%22">Touch screens</searchLink><br /><searchLink fieldCode="DE" term="%22Marketing%22">Marketing</searchLink><br /><searchLink fieldCode="DE" term="%22Human-computer+interaction%22">Human-computer interaction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In mobile touch interaction environments, a mismatch between marketing information presentation and users' cognitive load often results in suboptimal interaction experiences and low commercial conversion efficiency, thereby constraining the advancement of mobile marketing optimization. This study proposes an integrated technical framework that combines real-time multimodal cognitive load quantification with reinforcement learning-based adaptive decision-making to dynamically align marketing information presentation with users' real-time cognitive states. The framework consists of two core modules: a multimodal real-time cognitive load estimation model and a reinforcement learning-driven adaptive information presentation decision engine. The former synchronously collects multimodal data--including touch interaction behaviors, eye-tracking signals, and basic physiological indicators--and constructs a high-discriminability feature system integrated with a temporal multi-head attention fusion network. This design enables precise, millisecond-level cognitive load quantification without reliance on bulky laboratory equipment. The latter module treats cognitive load as the primary state signal, designs a multi-objective reward mechanism that balances shortterm user experience and long-term commercial value, and employs a cloud-edge collaborative deployment architecture to achieve dynamic and adaptive adjustment of marketing information presentation strategies. The two modules are tightly coupled through a real-time data streaming pipeline, effectively addressing the challenges of multimodal synchronization and low-latency decision-making in mobile environments. Experimental results in mobile marketing scenarios demonstrate that the proposed framework accurately captures users' real-time cognitive load, significantly optimizes information presentation effectiveness, and enhances both interaction experience and commercial conversion efficiency. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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=egs&AN=193831335 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijim.v20i09.61741 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 80 Subjects: – SubjectFull: Cognitive load Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Decision making Type: general – SubjectFull: Touch screens Type: general – SubjectFull: Marketing Type: general – SubjectFull: Human-computer interaction Type: general Titles: – TitleFull: Cognitive Load Modeling in Mobile Touch Interaction and Optimization of Marketing Information Presentation Strategies. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Sufeng – PersonEntity: Name: NameFull: Hu, Lili IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18657923 Numbering: – Type: volume Value: 20 – Type: issue Value: 9 Titles: – TitleFull: International Journal of Interactive Mobile Technologies Type: main |
| ResultId | 1 |
