<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...

Personalized Recommendation Multi-Objective Optimization Model Based on Deep Learning

Publié en ligne: 28 mars 2024

Pages: 44 - 57

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

Mots clés
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.

Personalized Recommendation Multi-Objective Optimization Model Based on Deep Learning

Publié en ligne: 28 mars 2024

Pages: 44 - 57

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

Mots clésRecommendation 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.

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