FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network
Abstract
:1. Introduction
- (1)
- We proposed a filter-enhanced MLP recommendation model based on the Generative Adversarial Network framework to solve the problem of recommendation bias. Through comparisons of two real-world datasets from Movielens and Ciao, we demonstrated the effectiveness of filter-enhanced MLP to improve data partitioning to address recommendation bias and achieve better results compared to baseline models.
- (2)
- We designed three different condition vectors to enhance the learning ability of the model and verified the influence of different condition vectors on the model effect through experimental comparison.
2. Related Work
2.1. Model-Based Collaborative Filtering
2.2. Debias Methods
2.3. Generative Adversarial Network and GAN-Based Recommendation
2.4. Filter-Enhanced Recommendation
2.5. Analytical Summary of the Literature
3. Methodology
3.1. Generator
3.2. Discriminator
3.3. Adversarial Training
3.4. FMGAN Recommendation
4. Experiment and Discussion
- Effect of embedding dimensions and linear layers.
- Effect of the filter.
- Model performance under different condition vectors.
- Performance comparison of the model with other benchmark models.
4.1. Experiment Setup
4.1.1. Datasets
4.1.2. Evaluation Metrics
4.1.3. Implementation Details
4.2. Discussion and Results
4.2.1. Effect of Embedding Dimensions and Linear Layers
4.2.2. Effect of the Filter
4.2.3. Model Performance under Different Condition Vectors
- ItemPop: A model that uses the most purchase records for recommendation, which is the simplest non-personalized algorithm method.
- BPR: The model pairs purchased items with unpurchased items and optimizes the correct ranking order between item pairs.
- FISM: The model uses two low-latitude latent vectors to simulate the item–item similarity matrix.
- IRGAN: The model applies the GAN architecture to the recommendation system and uses the binary classification loss to train point states or point pairs. This method is specifically introduced in Section 2.2.
- CFGAN: Improves the use of vectors as training parameters and introduces a sampling method to improve the training process. This method is specifically introduced in Section 2.2.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Statics | Movielens 100K | Movielens 1M | Ciao |
---|---|---|---|
users | 943 | 6039 | 996 |
items | 1682 | 3883 | 1927 |
ratings | 100,000 | 1,000,209 | 18,648 |
sparsity | 93.69% | 95.72% | 99.03% |
FMGAN Model Settings and Hyperparameters | ||||||||
---|---|---|---|---|---|---|---|---|
Dataset | lr | Hidden Layers | First Layer Input Size | Activation Function | Second Layer Input Size | Embedding Dimension | Batch Size | |
Movielens 100K | 0.3 | 1 × 10−4 | 2 | 3306 | ReLU | 1983 | 10 | 64 |
Movielens 1M | 0.3 | 3 × 10−4 | 2 | 7364 | ReLU | 4418 | 10 | 128 |
Ciao | 0.3 | 7 × 10−5 | 2 | 2694 | ReLU | 1616 | 10 | 64 |
Datasets | Model | Prec@5 | Recall@5 | NDCG@5 | MRR@5 |
---|---|---|---|---|---|
Movielens 100K | FMGAN-im | 0.467 | 0.154 | 0.496 | 0.698 |
MLP | 0.421 | 0.139 | 0.449 | 0.657 | |
Movielens 1M | FMGAN-im | 0.435 | 0.110 | 0.458 | 0.650 |
MLP | 0.402 | 0.101 | 0.425 | 0.629 | |
Ciao | FMGAN-im | 0.070 | 0.081 | 0.092 | 0.153 |
MLP | 0.061 | 0.071 | 0.086 | 0.146 |
Metrics | P@5 | P@20 | R@5 | R@20 | G@5 | G@20 | M@5 | M@20 |
---|---|---|---|---|---|---|---|---|
ItemPop | 0.182 | 0.139 | 0.105 | 0.253 | 0.165 | 0.196 | 0.255 | 0.293 |
BPR | 0.350 | 0.237 | 0.117 | 0.288 | 0.372 | 0.381 | 0.558 | 0.575 |
FISM | 0.428 | 0.285 | 0.146 | 0.354 | 0.464 | 0.429 | 0.675 | 0.686 |
IRGAN | 0.320 | 0.223 | 0.110 | 0.278 | 0.346 | 0.370 | 0.539 | 0.525 |
CFGAN | 0.445 | 0.327 | 0.149 | 0.360 | 0.477 | 0.440 | 0.682 | 0.701 |
FMGAN-ns | 0.446 | 0.330 | 0.145 | 0.359 | 0.473 | 0.437 | 0.675 | 0.698 |
FMGAN-im | 0.467 | 0.340 | 0.154 | 0.365 | 0.496 | 0.454 | 0.698 | 0.719 |
Metrics | P@5 | P@20 | R@5 | R@20 | G@5 | G@20 | M@5 | M@20 |
---|---|---|---|---|---|---|---|---|
ItemPop | 0.155 | 0.120 | 0.075 | 0.195 | 0.153 | 0.179 | 0.251 | 0.296 |
BPR | 0.340 | 0.251 | 0.075 | 0.207 | 0.347 | 0.361 | 0.535 | 0.554 |
FISM | 0.419 | 0.304 | 0.106 | 0.269 | 0.442 | 0.398 | 0.635 | 0.649 |
IRGAN | 0.262 | 0.213 | 0.071 | 0.165 | 0.265 | 0.245 | 0.302 | 0.337 |
CFGAN | 0.431 | 0.307 | 0.107 | 0.164 | 0.452 | 0.404 | 0.643 | 0.659 |
FMGAN-ns | 0.433 | 0.308 | 0.106 | 0.162 | 0.451 | 0.402 | 0.641 | 0.654 |
FMGAN-im | 0.435 | 0.311 | 0.110 | 0.169 | 0.458 | 0.406 | 0.650 | 0.662 |
Metrics | P@5 | P@20 | R@5 | R@20 | G@5 | G@20 | M@5 | M@20 |
---|---|---|---|---|---|---|---|---|
ItemPop | 0.030 | 0.023 | 0.039 | 0.126 | 0.046 | 0.063 | 0.054 | 0.067 |
BPR | 0.035 | 0.024 | 0.040 | 0.140 | 0.050 | 0.065 | 0.067 | 0.079 |
FISM | 0.060 | 0.037 | 0.071 | 0.179 | 0.080 | 0.110 | 0.127 | 0.147 |
IRGAN | 0.034 | 0.022 | 0.042 | 0.110 | 0.045 | 0.065 | 0.080 | 0.087 |
CFGAN | 0.068 | 0.040 | 0.079 | 0.190 | 0.089 | 0.117 | 0.151 | 0.164 |
FMGAN-ns | 0.065 | 0.039 | 0.076 | 0.186 | 0.087 | 0.113 | 0.149 | 0.163 |
FMGAN-im | 0.070 | 0.043 | 0.081 | 0.192 | 0.092 | 0.120 | 0.153 | 0.166 |
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Liu, Z.; Luo, W. FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network. Appl. Sci. 2023, 13, 7975. https://doi.org/10.3390/app13137975
Liu Z, Luo W. FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network. Applied Sciences. 2023; 13(13):7975. https://doi.org/10.3390/app13137975
Chicago/Turabian StyleLiu, Zhaoxuan, and Wenjie Luo. 2023. "FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network" Applied Sciences 13, no. 13: 7975. https://doi.org/10.3390/app13137975