Research on remote sensing information recommendation technology based on collaborative filtering
Yu-Chen Song, Lei Chang, Yuanchen Song, Xiaoming Zhou, Caiping Li, Xuefei Shi · Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering · 2021
With the rapid development of remote sensing platform technology, the amount of remote sensing data information has increased explosively. Solving the data recommendation for user specific behavior and application is the key to the application efficiency of remote sensing products. Aiming at the user's basic portrait model, this paper proposes a remote sensing information recommendation technology based on collaborative filtering, which uses the adjusted cosine after noise elimination to calculate the similarity between a product and another product. The noise elimination process added to this similarity calculation method is to subtract from the user's scoring mean, so that all scoring curves tend to be stable and ensure the accuracy and stability of the recommendation results. The application analysis shows that this method has certain feasibility and popularization, and is helpful to improve the overall efficiency of remote sensing application.