An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective

Abstract

With the development and popularization of remote sensing earth observation technology and the remote sensing satellite system, the problems of insufficient proactiveness, relevance and timeliness of large-scale remote sensing supporting services are increasingly prominent, which seriously restricts the application of remote sensing resources in multi-domain and cross-disciplinary. It is urgent to help terminal users make appropriate decisions according to real-time network environment and domain requirements, and obtain the optimal resources efficiently from the massive remote sensing resources. In this paper, we propose a recommendation algorithm using fusion of attention and multi-perspective (MRS_AMRA). Based on MRS_AMRA, we further implement an active service recommendation model (MRS_ASRM) for massive multi-source remote sensing resources by combining streaming pushing technology. Firstly, we construct value evaluation functions from multi-perspective in terms of remote sensing users, data and services to enable the adaptive provision of remote sensing resources. Then, we define multi-perspective heuristic policies to support resource discovery, and fusion these policies through the attention network, to achieve the accurate pushing of remote sensing resources. Finally, we implement comparative experiments to simulate accurate recommendation scenarios, compared with state-of-the-art algorithms, such as DIN and Geoportal. Furthermore, MRS_AMRA achieves an average improvement of 10.5% in the recommendation accuracy NDCG@K, and in addition, we developed a prototype system to verify the effectiveness and timeliness of MRS_ASRM.

Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation

“…Remote sensing resource service systems typically consist of various components, such as human–computer interaction, a resource portal, a category directory, resource services, and databases [ 1 ]. The system’s workflow is illustrated in Figure 1 , and it primarily functions by filtering resources using users’ search keywords, generating relevant results, and presenting them to users.…”

“…We compare TGDL-RSSR with traditional content-based retrieval methods [ 67 ] used in remote sensing resource service systems. We specifically look at the approach presented in [ 1 ]. We measure user satisfaction levels across three categories: zero, one, and two.…”

“…To address this challenge, remote sensing active service technology has emerged. This technology enables the analysis and exploration of vast remote sensing data, uncovering inherent patterns and subsequently generating a supportive service model [ 1 , 33 ].…”

“…The last layer is denoted as L and the second-to-last is denoted as L − 1. The output of these layers is denoted as {(e ′ f ) (1) , (e…”

“…Most current remote sensing information service systems rely on keyword search and subscription modes to provide users with the required resources [ 1 ]. In the search mode, users can enter specific query conditions and conduct searches accordingly.…”