A fuzzy data mining approach for remote sensing image recommendation

Lu, Erl-Huei; Hong, Jung-Hong; Su, Zeal Li-Tse; Chen, Chun‐Hao

doi:10.1109/grc.2013.6740410

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…One such solution is the FIR method, which was introduced by Lu et al. [ 35 ]. This method recommends and ranks remote sensing images based on users’ specific areas of interest by employing two special features.…”

Section: Related Workmentioning

confidence: 99%

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

Zhang,

Ma,

Zhang

et al. 2024

Sensors

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Technological progress has led to significant advancements in Earth observation and satellite systems. However, some services associated with remote sensing face issues related to timeliness and relevance, which affect the application of remote sensing resources in various fields and disciplines. The challenge now is to help end-users make precise decisions and recommendations for relevant resources that meet the demands of their specific domains from the vast array of remote sensing resources available. In this study, we propose a remote sensing resource service recommendation model that incorporates a time-aware dual LSTM neural network with similarity graph learning. We further use the stream push technology to enhance the model. We first construct interaction history behavior sequences based on users’ resource search history. Then, we establish a category similarity relationship graph structure based on the cosine similarity matrix between remote sensing resource categories. Next, we use LSTM to represent historical sequences and Graph Convolutional Networks (GCN) to represent graph structures. We construct similarity relationship sequences by combining historical sequences to explore exact similarity relationships using LSTM. We embed user IDs to model users’ unique characteristics. By implementing three modeling approaches, we can achieve precise recommendations for remote sensing services. Finally, we conduct experiments to evaluate our methods using three datasets, and the experimental results show that our method outperforms the state-of-the-art algorithms.

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Section: Related Workmentioning

confidence: 99%