An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/10/2564 |
| _version_ | 1827740278740484096 |
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| author | Lilu Zhu Feng Wu Kun Fu Yanfeng Hu Yang Wang Xinmei Tian Kai Huang |
| author_facet | Lilu Zhu Feng Wu Kun Fu Yanfeng Hu Yang Wang Xinmei Tian Kai Huang |
| author_sort | Lilu Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:21:44Z |
| format | Article |
| id | doaj.art-bcdefcef32ba4ac4b724c0b51d538040 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T03:21:44Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bcdefcef32ba4ac4b724c0b51d5380402023-11-18T03:06:56ZengMDPI AGRemote Sensing2072-42922023-05-011510256410.3390/rs15102564An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-PerspectiveLilu Zhu0Feng Wu1Kun Fu2Yanfeng Hu3Yang Wang4Xinmei Tian5Kai Huang6Suzhou Aerospace Information Research Institute, Suzhou 215123, ChinaSchool of Information Science and Technology, University of Science and Technology of China, Hefei 230026, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSuzhou Aerospace Information Research Institute, Suzhou 215123, ChinaSchool of Information Science and Technology, University of Science and Technology of China, Hefei 230026, ChinaSuzhou Aerospace Information Research Institute, Suzhou 215123, ChinaWith 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.https://www.mdpi.com/2072-4292/15/10/2564multi-source remote sensing informationmulti-perspective value evaluationattention mechanismcollaborative filteringrecommendation system |
| spellingShingle | Lilu Zhu Feng Wu Kun Fu Yanfeng Hu Yang Wang Xinmei Tian Kai Huang An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective Remote Sensing multi-source remote sensing information multi-perspective value evaluation attention mechanism collaborative filtering recommendation system |
| title | An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective |
| title_full | An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective |
| title_fullStr | An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective |
| title_full_unstemmed | An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective |
| title_short | An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective |
| title_sort | active service recommendation model for multi source remote sensing information using fusion of attention and multi perspective |
| topic | multi-source remote sensing information multi-perspective value evaluation attention mechanism collaborative filtering recommendation system |
| url | https://www.mdpi.com/2072-4292/15/10/2564 |
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