On Epidemic-aware Socio Spatial POI Recommendation

Epidemics such as COVID-19, SARS, H1N1 have highly transmissible viruses and spread wildly through the population with negative consequences. Multiple studies have shown the correlation between the contact networks between individuals and the transmission of infections due to contact between colocated individuals. To mitigate the transmission of the virus, intervention measures have been applied without decisive success. Therefore, reducing transmissions through suitable epidemicaware POI recommendations to users is necessary to cope with user mobility. Current POI recommendation approaches do not take into consideration the transmission of infections between co-located users. In this paper, we formulate a new query named Epidemic-aware POI Recommendation Query (EPQ), to timely recommend a set of POIs to users at different time steps, while considering the spread of infection between co-located users, their social friendships, and their preference. We prove that EPQ is NP-hard and propose an effective and efficient algorithm, Epidemic-aware POI Recommendation (EpRec) to tackle EPQ. We evaluate EpRec on existing location-based social networks and pandemic datasets against state-of-the-art algorithms. The experimental results show that EpRec outperforms the baselines in effectiveness and efficiency.