A semantic-aware collaborative filtering recommendation method for emergency plans in response to meteorological hazards.

Meteorological hazards have great influence all over the world. An emergency plan is an important means of coping with meteorological hazards. The preparation of emergency plans needs to refer to historical emergency plans, but these are too numerous and are of uneven quality. We can alleviate these problems by means of recommender systems, which are very useful tools in many domains; however, they suffer from information overload. In this paper, we propose a Semantic-Aware Collaborative Filtering method, which is called SACF, for emergency plans recommendation to address the aforementioned challenges. It is designed to effectively present a highly targeted emergency plan recommendation list and recommend the most appropriate emergency plans for a targeted meteorological hazards event. Specifically, we use semantic knowledge to represent scenario-based meteorological hazards, including target and previous events. The search for similar events (i.e., neighbors) for a collaborative filtering recommendation algorithm is adopted. By helping to avoid both the generation of fake neighbors and also the omission of true neighbors the recommendation process is improved. Finally, extensive experiments are conducted on a real-world dataset, and the results demonstrate that SACF improves the accuracy of emergency plan recommendations. [ABSTRACT FROM AUTHOR]