Open Access

Personalized Recommendation Multi-Objective Optimization Model Based on Deep Learning

Zepeng Yang

Zepeng Yang

School of Computer Science and Engineering Xi’an Technological UniversityXi’an, China
Search for this author on
Sciendo|Google Scholar
Yang, Zepeng
, 
Ping Lu

Ping Lu

School of Computer Science and Engineering Xi’an Technological UniversityXi’an, China
Search for this author on
Sciendo|Google Scholar
Lu, Ping
 and 
Pingping Liu

Pingping Liu

School of Computer Science and Engineering Xi’an Technological UniversityXi’an, China
Search for this author on
Sciendo|Google Scholar
Liu, Pingping
  
Mar 28, 2024
Personalized Recommendation Multi-Objective Optimization Model Based on Deep Learning's Cover Image
About this article
Previous Article
Next Article
Cite
Download Cover
Published Online: Mar 28, 2024
Page range: 44 - 57
DOI: https://doi.org/10.2478/ijanmc-2024-0005
Keywords
© 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.

Model structure of the Wide & Deep Learning model.
Model structure of the Wide & Deep Learning model.

Figure 2.

Shared-Bottom model.
Shared-Bottom model.

Figure 3.

FM_Shared Expert Multi-Objective Dependency (FSMD) Mode
FM_Shared Expert Multi-Objective Dependency (FSMD) Mode

Figure 4.

Multi-objective Base Model.
Multi-objective Base Model.

Figure 5.

Multi-gate MoE Model.
Multi-gate MoE Model.

Figure 6.

Internal Structure of Gated Network.
Internal Structure of Gated Network.

Figure 7.

FM_Gate (FG) Model.
FM_Gate (FG) Model.

Figure 8.

FM_Sharing the deep part of the Expert (FS) Model.
FM_Sharing the deep part of the Expert (FS) Model.

Figure 9.

FM_Shared Expert Multi-Objective Dependency(FSD)Model.
FM_Shared Expert Multi-Objective Dependency(FSD)Model.

Figure 10.

The seesaw phenomenon in each model under complex target association
The seesaw phenomenon in each model under complex target association

Figure 11.

AUC comparison of network shapes.
AUC comparison of network shapes.

Figure 12.

AUC comparison of the number of layers in a gated network.
AUC comparison of the number of layers in a gated network.

Figure 13.

AUC comparison of the number of experts.
AUC comparison of the number of experts.

PARAMETER SETTINGS

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

Results of the UCI Census Income Dataset

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

Parameter Settings

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
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Computer Sciences, other
Journal RSS Feed