Optimization of web content presentation poses a key challenge for e-commerce applications. Whether considering web pages, advertising banners or any other content presentation media on the web, the choice of the appropriate structure and appearance with respect to the given audience can obtain a more effective and successful impact on users, such as gathering more readers to web sites or customers to online shops. Here, the collective optimization of web content presentation based on the online discrete Particle Swarm Optimization (PSO) model is presented. The idea behind online PSO is to evaluate the collective user feedback as the PSO objective function which drives particles' velocities in the hybrid continuous-discrete space of web content features. The PSO coordinates the process of sampling collective user behaviour in order to optimize a given user-based 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 optima and hybrid discrete/continuous features management. The proposed online optimization method is sufficiently general and may be applied to other web marketing or business intelligence contexts.

Optimal design of web information contents for e-commerce applications

Santucci Valentino
;
2010

Abstract

Optimization of web content presentation poses a key challenge for e-commerce applications. Whether considering web pages, advertising banners or any other content presentation media on the web, the choice of the appropriate structure and appearance with respect to the given audience can obtain a more effective and successful impact on users, such as gathering more readers to web sites or customers to online shops. Here, the collective optimization of web content presentation based on the online discrete Particle Swarm Optimization (PSO) model is presented. The idea behind online PSO is to evaluate the collective user feedback as the PSO objective function which drives particles' velocities in the hybrid continuous-discrete space of web content features. The PSO coordinates the process of sampling collective user behaviour in order to optimize a given user-based 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 optima and hybrid discrete/continuous features management. The proposed online optimization method is sufficiently general and may be applied to other web marketing or business intelligence contexts.
9789048197934
collaborative intelligence; collective behaviour mining; Web marketing optimization; Industrial and Manufacturing Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12071/11141
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