Battlefield Situation Information Recommendation Based on Recall-Ranking

Abstract

With the rapid development of information technology, battlefield situation data presents the characteristics of “4V” such as Volume, Variety, Value and Velocity. While enhancing situational awareness, it also brings many challenges to battlefield situation information recommendation (BSIR), such as big data volume, high timeliness, implicit feedback and no negative feedback. Focusing on the challenges faced by BSIR, we propose a two-stage BSIR model based on deep neural network (DNN). The model utilizes DNN to extract the nonlinear relationship between the data features effectively, mine the potential content features, and then improves the accuracy of recommendation. These two stages are the recall stage and the ranking stage. In the recall stage, the candidate set of situation information is generated, and the massive situation information is reduced to a small candidate subset that is highly relevant to the commanders. In the ranking stage, the situation information in the candidate set is accurately scored and then sorted, and the situation information with a high score is eventually recommended to the commanders. Finally, we use the historical date from the situation management of real combat training information systems as the dataset and verify the effective-ness of the algorithm through experiments, and analyze the effects of the depth and width of the neural network on the performance of the algorithm.