A Geospatial Data Recommender System based on Metadata and User Behaviour

Earth observations are produced in a fast velocity through real time sensors, reaching tera- to peta- bytes of geospatial data daily. Discovering and accessing the right data from the massive geospatial data is like finding needle in the haystack. To help researchers find the right data for study and decision support, quite a lot of research focusing on improving search performance have been proposed including recommendation algorithm. However, few papers have discussed the way to implement a recommendation algorithm in geospatial data retrieval system. In order to address this problem, we propose a recommendation engine to improve discovering relevant geospatial data by mining and utilizing metadata and user behavior data: 1) metadata based recommendation considers the correlation of each attribute (i.e., spatiotemporal, categorical, and ordinal) to data to be found. In particular, phrase extraction method is used to improve the accuracy of the description similarity; 2) user behavior data are utilized to predict the interest of a user through collaborative filtering; 3) an integration method is designed to combine the results of the above two methods to achieve better recommendation Experiments show that in the hybrid recommendation list, the all the precisions are larger than 0.8 from position 1 to 10.