Skip or Stay: Users' Behavior in Dealing with Onsite Information Interaction Crowd-Bias

Mobile devices and the internet of things blend our virtual online behavior with our actions in the real-world. The physical context creates numerous external factors that play a role in the user's online interactions, thus creating new external biases in the collected information interaction logs. Our general aim is to improve our understanding of onsite users' behavior, which allow us to create better online and onsite contextual suggestion systems. We focus on the cultural heritage domain and have collected onsite users' information interaction logs of visits in a museum. This prompts the question: How to understand users' behavior in order to be able to predict their onsite behaviors? Our main findings are the following: First, users behave differently in different onsite contextual situations. Second, there is a significant dependency between users' onsite behaviors and other users, who are interacting with next point of interests (POIs). Third, we have proposed a contextual Skip-Or-Stay behavior classifier based on four different pairwise contextual features that significantly improves a defined baseline based on all the considered evaluation metrics. Fourth, we have analyzed the importance of different contexts in the Skip-or-Stay behavior predictions.