Cited By
View all- Zhang JMa WZhang EXia X(2024)Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service RecommendationSensors10.3390/s2404118524:4(1185)Online publication date: 11-Feb-2024
Adaptive recommendation technology for remote sensing information based on behavior analysis
Authors: 
Caiping Li, Lei Chang, Jisheng Zhang, Xiaoming Zhou, Zhen Hu, Yuanchen SongAuthors Info & Claims
Article No.: 50, Pages 1 - 5
Abstract
In view of the changing interests of users of remote sensing information, this paper proposes a self-adaptive recommendation technology for remote sensing information based on behavior analysis. Through real-time collection and feedback of user's browsing, querying and downloading behavior data on the application platform, users can find new concerns and construct attenuation functions. Periodically, users' preferences are revised. The adaptive learning strategy adds users' new preferences to the key information structure, which strengthens the original information preferences. This technology not only takes full account of users' historical interest points, but also dynamically discovers users' new interests, and integrates them reasonably according to interest attenuation model, so as to improve the accuracy of recommendation.
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Index Terms
- Adaptive recommendation technology for remote sensing information based on behavior analysis
Recommendations
Research on remote sensing information recommendation technology based on collaborative filtering
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December 2019
464 pages
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Published: 19 December 2019
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AIIPCC '19
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AIIPCC '19: 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing
December 19 - 21, 2019
Sanya, China
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AIIPCC '19 Paper Acceptance Rate 78 of 211 submissions, 37%;
Overall Acceptance Rate 78 of 211 submissions, 37%
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View all- Zhang JMa WZhang EXia X(2024)Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service RecommendationSensors10.3390/s2404118524:4(1185)Online publication date: 11-Feb-2024
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