Optimizing web content presentation: A online PSO approach

Alfredo Milani*, Valentino Santucci, Clement H C LEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In this paper we propose an approach to optimization of web marketing content based on online discrete particle swarm optimization (PSO) model. The idea behind online PSO is to evaluate the collective user feedback as the PSO objective function which drive particles velocities in the hybrid continuous-discrete space of web content features. The PSO coordinate the process of sampling collective user behavior in order to optimize the web marketing metric. Experiments in the online banner optimization scenario show that the method converges faster than other methods and avoid some common drawbacks such as local optimal and hybrid discrete/continuous features management. The proposed online optimization method is general and can be applied to other web marketing or business intelligent contexts.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009
Pages26-29
Number of pages4
DOIs
Publication statusPublished - 2009
Event2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009 - Milano, Italy
Duration: 15 Sep 200918 Sep 2009

Publication series

NameProceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009
Volume3

Conference

Conference2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009
Country/TerritoryItaly
CityMilano
Period15/09/0918/09/09

Scopus Subject Areas

  • Computer Networks and Communications
  • Software

User-Defined Keywords

  • Collaborative intelligence
  • Collective behavior mining
  • Web marketing optimization

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