<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Recommended in this paper, because the existing single objective experience is poor, and the recommended model in a large difference of targets under the complex relationship of joint optimization and the conflict caused by faults, this paper proposes a personalized recommendation based on the deep learning multi-objective optimization algorithm, the estimated probability of users on the individual behavior as a model to study target, Multiple objectives are integrated into a model for learning. Firstly, the embedding layer is used to change the feature vectors, so that the bottom layer of the model shares the same feature embedding. Secondly, the factorization machine and deep learning are used to construct high-low order feature interaction. Then, the gating network and multilevel expert network constructed by a fully connected neural network are used to learn the characteristic relationship of user behavior. Finally, make connections between goals. Through experiments on public and real datasets, The results show that the multi-objective model proposed in this paper has better co-optimization performance and increases the AUC value by 0.1% compared with advanced personalized recommendation models such as MMoE and ESMM, to achieve the ultimate goal of increasing the prediction accuracy and improving user satisfaction.</p>
</abstract>

Recommended in this paper, because the existing single objective experience is poor, and the recommended model in a large difference of targets under the complex relationship of joint optimization and the conflict caused by faults, this paper proposes a personalized recommendation based on the deep learning multi-objective optimization algorithm, the estimated probability of users on the individual behavior as a model to study target, Multiple objectives are integrated into a model for learning. Firstly, the embedding layer is used to change the feature vectors, so that the bottom layer of the model shares the same feature embedding. Secondly, the factorization machine and deep learning are used to construct high-low order feature interaction. Then, the gating network and multilevel expert network constructed by a fully connected neural network are used to learn the characteristic relationship of user behavior. Finally, make connections between goals. Through experiments on public and real datasets, The results show that the multi-objective model proposed in this paper has better co-optimization performance and increases the AUC value by 0.1% compared with advanced personalized recommendation models such as MMoE and ESMM, to achieve the ultimate goal of increasing the prediction accuracy and improving user satisfaction.

Personalized Recommendation Multi-Objective Optimization Model Based on Deep Learning

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Recommended in this paper, because the existing single objective experience is poor, and the recommended model in a large difference of targets under the...

Parameter Name	Value
batch_size	256
optimizer	adam
learning_rate	0.001
embedding_size	4
dnn_layers	(512, 256)
dnn_use_bn	True

Models	AUC/ Income	AUC/ Marital	Mean
Single-Task	0.9198	0.9748	0.9473
Shared-Bottom	0.9148	0.9754	0.9451
MMoE	0.9152	0.9756	0.9454
PLE	0.9161	0.9764	0.9463
Base	0.9134	0.9706	0.9420
FG	0.9146	0.9693	0.9420
FS	0.9216	0.9756	0.9486

Models	AUC/ Income	AUC/ Marital	Mean
Single-Task	0.7192	0.6635	0.6914
DeepFM	0.719	0.658	0.689
Shared-Bottom	0.7184	0.6916	0.705
ESMM	0.7189	0.6897	0.7043
MMoE	0.7201	0.7057	0.7129
Base	0.7193	0.7086	0.714
FG	0.7201	0.7084	0.7143
FS	0.7204	0.7114	0.7159
FSMD	0.7205	0.7117	0.7161

Personalized Recommendation Multi-Objective Optimization Model Based on Deep Learning

Published Online: Mar 28, 2024

Page range: 44 - 57

DOI: https://doi.org/10.2478/ijanmc-2024-0005

Keywords
Recommendation Algorithm, Multi-Objective Optimization, Post-Click Conversion Rate, Deep Learning, The Neural Network

© 2024 Zepeng Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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PARAMETER SETTINGS

Results of the UCI Census Income Dataset

Parameter Settings

Personalized Recommendation Multi-Objective Optimization Model Based on Deep Learning

Published Online: Mar 28, 2024

Page range: 44 - 57

DOI: https://doi.org/10.2478/ijanmc-2024-0005

KeywordsRecommendation Algorithm, Multi-Objective Optimization, Post-Click Conversion Rate, Deep Learning, The Neural Network

© 2024 Zepeng Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Figure 12.

Figure 13.

PARAMETER SETTINGS

Results of the UCI Census Income Dataset

Parameter Settings

Keywords
Recommendation Algorithm, Multi-Objective Optimization, Post-Click Conversion Rate, Deep Learning, The Neural Network