Mobility-based Individual POI Recommendation to Control the COVID-19 Spread

Abstract

Societal functions have stalled during COVID-19 to reduce its spread in the population. It has been shown that visits to different venues have a large effect on spreading the virus. Hence, population-level mobility interventions like reopening selective category of venues have been proposed, for example, opening schools and offices but preventing people from visiting restaurants. These measures, although help to mitigate infection, still fail to satisfy people’s needs and hope of going back to normality. In this context, here we propose an individual level POI recommendation system that can recommend venues to users according to their preference and at the same time, can lead to as few infections as possible. The key idea behind the system is that the risk of getting infected grows with the number of unique customers that had visited the venue previously, and it is safer to visit a less crowded place during a specific time slot. We evaluate the proposed system using both theory and real check-in datasets from three cities. Based on simulation on real-world data, we present a surprising result: it is possible to recommend POIs in such a way that the total infected population reduces by up to 50% compared to that following original check-ins. This result is comparable to that when 50% of the visits are blocked, yet our method allows all check-in needs.

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