Online PSO for Web Marketing Optimization

In this paper we propose an approach to optimization of web marketing content based on an online particle swarm optimization (PSO) model. 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. PSO coordinates the process of sampling collective user behavior in order to optimize the web marketing metric. To improve the performances a variation to the PSO schema is adopted, this variation consists in a restart of the algorithm if the convergence speed is not good. Experiments in the scenario of the home page of an online shop show that the method converges faster and avoid some common drawbacks such as local optimal and hybrid discrete/continuous features management; however is observed that the restart procedure improves the convergence speed of some difficult instances of the problem without affects the other ones. The proposed online optimization method is general and can be applied to other web marketing or business intelligent contexts.