Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation
Abstract
:1. Introduction
- We propose a method that utilizes LSTM to capture the dynamic relationships between search items and construct a sequence of user search history behavior.
- We present a method that utilizes CF to create a similarity matrix between remote sensing resource categories and then uses an MLP to generate embedded representations based on the matrix.
- We utilize GCN to model both the graph structure and similarity embeddings by establishing an adjacency graph relationship among categories.
- We utilize LSTM to capture the temporal dynamical similarity between categories by combining historical behavior sequences with GCN modeling results.
2. Related Work
3. Proposed Method
3.1. Problem Formulation
3.2. Definitions
3.3. Overall Framework
3.4. Input Layer
3.5. Representation Layer
3.5.1. Long Short-Term Interest Representation of Users’ Historical Behavior Sequences
Algorithm 1 Long short-term interest representation of users’ historical behavior sequences |
|
3.5.2. Time-Dynamic Representation of Similarity Relationships among Remote Sensing Resource Categories
3.5.3. Representation of Potential Influencing Factors for Unique User Identification ID
3.6. Concatenation Layer
Algorithm 2 Time-dynamic representation of similarity relationships among remote sensing resource categories |
|
3.7. Multilayer Perceptron Layer
3.8. Output Layer
3.9. Optimization
3.10. Deployment of the Recommendation Algorithm
4. Experiments
4.1. Experimental Setting
4.1.1. Experimental Environment
4.1.2. Public Recommendation Datasets
4.1.3. Remote Sensing Service Dataset
4.1.4. Data Preprocessing
4.1.5. Parameter Settings
4.1.6. Evaluation Metrics
4.2. Experiments on Recommender System Datasets
4.2.1. Overall Comparison with Baseline Methods
4.2.2. Sensitivity Analysis
4.2.3. Ablation Study
4.2.4. Computational Efficiency
4.3. Experiments on Remote Sensing Service Dataset
4.3.1. Overall Comparison with Baseline Methods
4.3.2. Usability Experiments
4.3.3. User Satisfaction Comparison
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Methods | Sensor Type | Application Area |
---|---|---|
Optical sensor | Visible and infrared spectrum | Environmental monitoring, Agriculture, Urban planning |
Radar sensor | Microwave radiation | Natural disaster monitoring, Resource management |
Thermal infrared sensor | Infrared spectrum | Surface temperature monitoring, Vegetation health assessment |
Active Service Methods | Description | Examples |
---|---|---|
Catalog Searching | Utilizes library catalogs, databases | Library catalogs, academic databases |
Surveys and Interviews | Engages in communication with domain experts or practitioners | Professionals, industry practitioners |
Archival Research | Retrieves historical records, documents | Archives, historical reports |
On-site Investigations | Conducts site visits, observes and records | Field inspections, survey reports |
Notations | Descriptions |
---|---|
User/remote sensing resources | |
ID of a specific user | |
Interaction between the user and remote sensing resources | |
Similarity matrix among remote sensing resource categories | |
Adjacency relationship among remote sensing resource categories | |
{} | users’ historical behavior sequence |
Datasets | MovieLens | Amazon-Clothes | Amazon-Books |
---|---|---|---|
Number of users | 6040 | 4993 | 47,400 |
Number of remote sensing resources | 3706 | 39 | 36,412 |
Data sparsity | 95.53% | 96.82% | 99.99% |
Datasets | Models | k = 5 | k = 10 | k = 15 | k = 20 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Recall | Precision | F1 | Recall | Precision | F1 | Recall | Precision | F1 | Recall | Precision | F1 | ||
MovieLens | CF | 0.0755 | 0.0151 | 0.0252 | 0.1384 | 0.0138 | 0.0252 | 0.1824 | 0.0122 | 0.0228 | 0.2327 | 0.0116 | 0.0222 |
NCF | 0.0913 | 0.0182 | 0.0305 | 0.1633 | 0.0163 | 0.0297 | 0.2202 | 0.0147 | 0.0275 | 0.2593 | 0.0129 | 0.0247 | |
LSTM | 0.0978 | 0.0196 | 0.0325 | 0.1889 | 0.0189 | 0.0343 | 0.2511 | 0.0167 | 0.0314 | 0.3578 | 0.0179 | 0.0341 | |
AGCN | 0.1452 | 0.0290 | 0.0484 | 0.2253 | 0.0225 | 0.0410 | 0.2718 | 0.0181 | 0.0340 | 0.3195 | 0.0160 | 0.0304 | |
DCF | 0.1523 | 0.0304 | 0.0508 | 0.2461 | 0.0246 | 0.0447 | 0.3041 | 0.0203 | 0.0380 | 0.3800 | 0.0190 | 0.0362 | |
TGDL-RSSR | 0.1565 | 0.0313 | 0.0522 | 0.2575 | 0.0257 | 0.0468 | 0.3165 | 0.0211 | 0.0396 | 0.4325 | 0.0216 | 0.0412 | |
Amazon-clothes | %Improv. | 2.76% | 2.96% | 2.76% | 4.63% | 4.47% | 4.70% | 4.08% | 3.94% | 4.21% | 13.82% | 13.68% | 13.81% |
CF | 0.0494 | 0.0099 | 0.0165 | 0.1015 | 0.0102 | 0.0185 | 0.1439 | 0.0096 | 0.0180 | 0.2186 | 0.0109 | 0.0208 | |
NCF | 0.0841 | 0.0168 | 0.0281 | 0.1362 | 0.0136 | 0.0248 | 0.1805 | 0.0120 | 0.0226 | 0.2423 | 0.0121 | 0.0231 | |
LSTM | 0.0831 | 0.0166 | 0.0277 | 0.1592 | 0.0159 | 0.0289 | 0.2504 | 0.0167 | 0.0313 | 0.3444 | 0.0172 | 0.0328 | |
AGCN | 0.1324 | 0.0221 | 0.0378 | 0.1809 | 0.0181 | 0.0329 | 0.2362 | 0.0157 | 0.0295 | 0.2987 | 0.0149 | 0.0284 | |
DCF | 0.1061 | 0.0212 | 0.0354 | 0.1944 | 0.0194 | 0.0353 | 0.2748 | 0.0183 | 0.0343 | 0.3470 | 0.0173 | 0.0330 | |
TGDL-RSSR | 0.1465 | 0.0244 | 0.0418 | 0.2140 | 0.0214 | 0.0389 | 0.2925 | 0.0195 | 0.0366 | 0.3674 | 0.0184 | 0.0350 | |
Amazon-books | %Improv. | 10.65% | 10.41% | 10.58% | 10.08% | 10.31% | 10.20% | 6.44% | 6.56% | 6.71% | 5.88% | 6.36% | 6.06% |
CF | 0.0744 | 0.0149 | 0.0248 | 0.1349 | 0.0135 | 0.0245 | 0.2186 | 0.0146 | 0.0273 | 0.2930 | 0.0147 | 0.0279 | |
NCF | 0.0791 | 0.0158 | 0.0263 | 0.1823 | 0.0182 | 0.0332 | 0.2586 | 0.0172 | 0.0323 | 0.3805 | 0.0190 | 0.0362 | |
LSTM | 0.1130 | 0.0226 | 0.0377 | 0.2300 | 0.0230 | 0.0418 | 0.3223 | 0.0215 | 0.0403 | 0.4093 | 0.0172 | 0.0390 | |
AGCN | 0.1407 | 0.0281 | 0.0469 | 0.2488 | 0.0249 | 0.0452 | 0.3387 | 0.0226 | 0.0423 | 0.4243 | 0.0212 | 0.0404 | |
DCF | 0.1354 | 0.0271 | 0.0452 | 0.2611 | 0.0261 | 0.0475 | 0.3678 | 0.0245 | 0.0460 | 0.4649 | 0.0233 | 0.0443 | |
TGDL-RSSR | 0.1487 | 0.0297 | 0.0496 | 0.2927 | 0.0293 | 0.0532 | 0.3933 | 0.0262 | 0.0492 | 0.4673 | 0.0234 | 0.0445 | |
%Improv. | 5.68% | 5.69% | 5.76% | 12.10% | 12.26% | 12.00% | 6.93% | 6.94% | 6.96% | 5.16% | 4.29% | 4.51% |
Datasets | Models | k = 5 | k = 10 | k = 15 | k = 20 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Recall | Precision | F1 | Recall | Precision | F1 | Recall | Precision | F1 | Recall | Precision | F1 | ||
Remote Sensing | CF | 0.0733 | 0.0147 | 0.0244 | 0.1933 | 0.0193 | 0.0352 | 0.2600 | 0.0173 | 0.0325 | 0.3200 | 0.0160 | 0.0305 |
NCF | 0.0868 | 0.0174 | 0.0289 | 0.1837 | 0.0184 | 0.0334 | 0.2780 | 0.0185 | 0.0348 | 0.3744 | 0.0187 | 0.0357 | |
LSTM | 0.1062 | 0.0212 | 0.0354 | 0.1948 | 0.0195 | 0.0354 | 0.2641 | 0.0176 | 0.0330 | 0.4245 | 0.0212 | 0.0404 | |
AGCN | 0.1117 | 0.0224 | 0.0372 | 0.2001 | 0.0200 | 0.0364 | 0.2963 | 0.0198 | 0.0370 | 0.3966 | 0.0198 | 0.0378 | |
DCF | 0.1293 | 0.0259 | 0.0431 | 0.2329 | 0.0233 | 0.0424 | 0.3365 | 0.0224 | 0.0421 | 0.4461 | 0.0223 | 0.0425 | |
TGDL-RSSR | 0.1279 | 0.0256 | 0.0426 | 0.2610 | 0.0261 | 0.0475 | 0.3576 | 0.0238 | 0.0447 | 0.5190 | 0.0260 | 0.0494 | |
%Improv. | −1.08% | −1.16% | −1.16% | 12.07% | 12.02% | 12.03% | 06.27% | 06.25% | 06.18% | 16.34% | 16.59% | 16.24% |
Comparison Items | Active Service Recommendation Model | Content-Based Retrieval Method |
---|---|---|
Service mode | Proactive | Passive |
User satisfaction | 2 | 0 |
System usage time | 4.5 min | 8.3 min |
System latency time | 0.58 s | 1.13 s |
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Zhang, J.; Ma, W.; Zhang, E.; Xia, X. Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation. Sensors 2024, 24, 1185. https://doi.org/10.3390/s24041185
Zhang J, Ma W, Zhang E, Xia X. Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation. Sensors. 2024; 24(4):1185. https://doi.org/10.3390/s24041185
Chicago/Turabian StyleZhang, Jinkai, Wenming Ma, En Zhang, and Xuchen Xia. 2024. "Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation" Sensors 24, no. 4: 1185. https://doi.org/10.3390/s24041185
APA StyleZhang, J., Ma, W., Zhang, E., & Xia, X. (2024). Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation. Sensors, 24(4), 1185. https://doi.org/10.3390/s24041185