A Bayesian Model for Activities Recommendation and Event Structure Optimization Using Visitors Tracking
This work addresses event management challenges for organizers by improving activity placement and visitor recommendations, but it is incremental as it builds on existing Complex Network theory.
The paper tackled the problem of managing visitor information and optimizing event structures with multiple activities, achieving ~95% accuracy in a recommendation system and showing better optimization than random methods.
In events that are composed by many activities, there is a problem that involves retrieve and management the information of visitors that are visiting the activities. This management is crucial to find some activities that are drawing attention of visitors; identify an ideal positioning for activities; which path is more frequented by visitors. In this work, these features are studied using Complex Network theory. For the beginning, an artificial database was generated to study the mentioned features. Secondly, this work shows a method to optimize the event structure that is better than a random method and a recommendation system that achieves ~95% of accuracy.