Collaborative Filtering Recommendation Algorithm of Weaponry Based on Knowledge Graph
Xiao Li, Desheng Liu, Qing Chang · 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · 2021
Nowadays, modern warfare has changed from weapon-centric operations to network-centric system operations, which has led to the phenomenon of sea-level quantification of weaponry. It is an important research topic for combat commanders to select suitable weaponry from a large number of available resources when facing combat tasks. Recommendation technology is a solution to this problem, which can effectively solve the problem of information overload, so that combat commanders do not produce the phenomenon of “information loss”, in the shortest possible time to make the right decision. Therefore, this paper proposes a collaborative filtering recommendation algorithm for weaponry based on the knowledge graph and the idea of collaborative filtering. In this algorithm, the knowledge graph provides a clear data model with various association relationships, and the collaborative filtering algorithm can realize it without relying on professional experience, which can realize the recommendation process more actively, faster and more intelligently.